STRIVE
Descriptive Stats
Hongxu Wang
31 July 2024
#data1 <- data %>% drop_na(case_control) %>% dyplr::select(-strive_id,-contains("scld")) %>%
# mutate_at(.vars = potential_conf[-2],
# .funs = ~ as.character(.))
#
# table1::table1(~.|case_control, data1)Check percentage of NA for each PFAS
data_pfas<- data %>% dplyr::select(pfas_name, -contains("scld"))
## Stats----
pfas_na <- data_pfas %>%
pivot_longer(cols = pfas_name) %>%
group_by(name) %>%
dplyr::summarize(pct_na = sum(is.na(value))/dim(data_pfas)[1])%>%
ungroup()%>%
arrange(pct_na)
print(pfas_na, n=25) #n=25## # A tibble: 25 × 2
## name pct_na
## <chr> <dbl>
## 1 pf_do_a 0
## 2 pf_hp_a 0
## 3 pf_hp_s 0
## 4 pf_hx_a 0
## 5 pf_hx_s 0
## 6 pf_pe_a 0
## 7 pf_pe_s 0
## 8 pf_un_a 0
## 9 pfba 0
## 10 pfbs 0
## 11 pfda 0
## 12 pfna 0
## 13 pfoa 0
## 14 pfos 0
## 15 x9cl_pf3ons 0.267
## 16 x8_2fts 0.939
## 17 hfpo_da 0.944
## 18 pfeesa 0.950
## 19 x6_2fts 0.979
## 20 x11cl_pf3o_ud_s 0.989
## 21 adona 0.992
## 22 nfdha 0.997
## 23 pfmba 0.997
## 24 pfmpa 0.997
## 25 x4_2fts 0.997correlation_matrix <- data_pfas %>%
select_if(~sum(!is.na(.)) > 0) %>% # Ensure there is at least some non-missing data
cor(use = "pairwise.complete.obs")
print(correlation_matrix)## pfba pf_pe_a pf_hx_a pf_hp_a pfoa
## pfba 1.000000000 0.0543499400 -0.050386280 0.25003054 -0.05723167
## pf_pe_a 0.054349940 1.0000000000 0.050463834 -0.05439341 -0.07981039
## pf_hx_a -0.050386280 0.0504638340 1.000000000 0.14482447 -0.09012840
## pf_hp_a 0.250030540 -0.0543934086 0.144824473 1.00000000 0.33935398
## pfoa -0.057231673 -0.0798103861 -0.090128401 0.33935398 1.00000000
## pfna -0.066845973 -0.0598405991 -0.106040612 0.19186209 0.76335319
## pfda 0.011123974 0.0047841577 -0.115760252 0.18264936 0.53127553
## pf_un_a 0.021508401 -0.0002736169 -0.140203638 0.13481229 0.32779881
## pf_do_a 0.092717740 0.0080579055 -0.090936382 0.14068137 0.22460958
## hfpo_da 0.073148569 0.1847227651 0.009064506 -0.31529695 -0.25073689
## pfbs 0.212656172 -0.0300843635 0.011924920 0.48634102 0.07880220
## pf_pe_s -0.001061104 -0.0568650696 -0.010201890 0.47944494 0.47473027
## pf_hx_s -0.032329599 -0.0810383044 -0.071786764 0.29696176 0.53757395
## pf_hp_s -0.038415970 -0.0791630751 -0.142705592 0.22368108 0.55092467
## pfos -0.015922406 -0.0235359965 -0.130012978 0.17686502 0.44506674
## x4_2fts NA NA NA NA NA
## x6_2fts -0.403054453 0.3122922386 -0.274737535 -0.12810615 0.21735843
## x8_2fts -0.029653414 0.0351079929 0.505409321 -0.15938202 0.15189179
## pfmpa NA NA NA NA NA
## pfmba NA NA NA NA NA
## adona NA 0.8264867649 -0.466321371 0.79427736 0.98423933
## pfeesa -0.464329780 -0.3832481791 0.166024353 -0.08926641 -0.23027405
## nfdha NA NA NA NA NA
## x9cl_pf3ons -0.015998696 0.0494809359 -0.006153293 0.02768365 0.10327682
## x11cl_pf3o_ud_s NA 0.6159388044 0.228687955 0.70768872 0.21369162
## pfna pfda pf_un_a pf_do_a
## pfba -0.066845973 0.011123974 0.0215084011 0.092717740
## pf_pe_a -0.059840599 0.004784158 -0.0002736169 0.008057905
## pf_hx_a -0.106040612 -0.115760252 -0.1402036384 -0.090936382
## pf_hp_a 0.191862093 0.182649363 0.1348122875 0.140681373
## pfoa 0.763353190 0.531275533 0.3277988109 0.224609575
## pfna 1.000000000 0.791980024 0.5617595563 0.404655974
## pfda 0.791980024 1.000000000 0.8187452278 0.724313889
## pf_un_a 0.561759556 0.818745228 1.0000000000 0.760852031
## pf_do_a 0.404655974 0.724313889 0.7608520307 1.000000000
## hfpo_da -0.140740646 -0.135958675 -0.1928757253 0.196778846
## pfbs 0.046710633 0.073781847 0.0898649439 0.125017110
## pf_pe_s 0.196594819 0.221581293 0.2237770503 0.174840064
## pf_hx_s 0.296106399 0.215283880 0.2182507466 0.102323446
## pf_hp_s 0.565898358 0.534993490 0.4842219935 0.364253692
## pfos 0.611070725 0.757998359 0.7104051311 0.622260918
## x4_2fts NA NA NA NA
## x6_2fts 0.245508321 0.204418331 0.0966787444 -0.035523245
## x8_2fts 0.006179123 0.105075259 0.0173320779 0.027663920
## pfmpa NA NA NA NA
## pfmba NA NA NA NA
## adona 0.800797338 0.994893541 0.9973266730 0.842731002
## pfeesa -0.148790453 -0.270509659 -0.2709701487 -0.100721712
## nfdha NA NA NA NA
## x9cl_pf3ons 0.162233086 0.358111251 0.3474849412 0.184391389
## x11cl_pf3o_ud_s 0.410018943 0.492054307 0.4262740527 0.340938836
## hfpo_da pfbs pf_pe_s pf_hx_s pf_hp_s
## pfba 0.073148569 0.212656172 -0.001061104 -0.03232960 -0.03841597
## pf_pe_a 0.184722765 -0.030084364 -0.056865070 -0.08103830 -0.07916308
## pf_hx_a 0.009064506 0.011924920 -0.010201890 -0.07178676 -0.14270559
## pf_hp_a -0.315296951 0.486341016 0.479444944 0.29696176 0.22368108
## pfoa -0.250736889 0.078802197 0.474730274 0.53757395 0.55092467
## pfna -0.140740646 0.046710633 0.196594819 0.29610640 0.56589836
## pfda -0.135958675 0.073781847 0.221581293 0.21528388 0.53499349
## pf_un_a -0.192875725 0.089864944 0.223777050 0.21825075 0.48422199
## pf_do_a 0.196778846 0.125017110 0.174840064 0.10232345 0.36425369
## hfpo_da 1.000000000 -0.155043725 -0.111465998 -0.14685128 0.16657959
## pfbs -0.155043725 1.000000000 0.135070072 0.05422040 0.06501304
## pf_pe_s -0.111465998 0.135070072 1.000000000 0.67295348 0.45729373
## pf_hx_s -0.146851282 0.054220398 0.672953480 1.00000000 0.70293011
## pf_hp_s 0.166579594 0.065013044 0.457293728 0.70293011 1.00000000
## pfos 0.074086233 0.071541404 0.319466233 0.46909749 0.86330712
## x4_2fts NA NA NA NA NA
## x6_2fts NA -0.268907342 0.010578642 0.02076347 0.16638840
## x8_2fts -1.000000000 -0.299861487 0.225333198 0.18584808 0.31338227
## pfmpa NA NA NA NA NA
## pfmba NA NA NA NA NA
## adona NA 0.999260081 0.960768923 0.99625643 0.66158999
## pfeesa NA 0.180493414 -0.180934765 -0.25296145 -0.31630560
## nfdha NA NA NA NA NA
## x9cl_pf3ons -0.036500205 -0.009044973 0.179528111 0.21782378 0.18051926
## x11cl_pf3o_ud_s NA 0.757896416 0.743341943 0.40193716 0.49731826
## pfos x4_2fts x6_2fts x8_2fts pfmpa pfmba
## pfba -0.01592241 NA -0.40305445 -0.029653414 NA NA
## pf_pe_a -0.02353600 NA 0.31229224 0.035107993 NA NA
## pf_hx_a -0.13001298 NA -0.27473753 0.505409321 NA NA
## pf_hp_a 0.17686502 NA -0.12810615 -0.159382021 NA NA
## pfoa 0.44506674 NA 0.21735843 0.151891788 NA NA
## pfna 0.61107072 NA 0.24550832 0.006179123 NA NA
## pfda 0.75799836 NA 0.20441833 0.105075259 NA NA
## pf_un_a 0.71040513 NA 0.09667874 0.017332078 NA NA
## pf_do_a 0.62226092 NA -0.03552324 0.027663920 NA NA
## hfpo_da 0.07408623 NA NA -1.000000000 NA NA
## pfbs 0.07154140 NA -0.26890734 -0.299861487 NA NA
## pf_pe_s 0.31946623 NA 0.01057864 0.225333198 NA NA
## pf_hx_s 0.46909749 NA 0.02076347 0.185848083 NA NA
## pf_hp_s 0.86330712 NA 0.16638840 0.313382273 NA NA
## pfos 1.00000000 NA 0.16197363 0.243848793 NA NA
## x4_2fts NA NA NA NA NA NA
## x6_2fts 0.16197363 NA 1.00000000 NA NA NA
## x8_2fts 0.24384879 NA NA 1.000000000 NA NA
## pfmpa NA NA NA NA NA NA
## pfmba NA NA NA NA NA NA
## adona 0.59389911 NA NA NA NA NA
## pfeesa -0.31206574 NA NA NA NA NA
## nfdha NA NA NA NA NA NA
## x9cl_pf3ons 0.27195850 NA -0.06401844 -0.235970171 NA NA
## x11cl_pf3o_ud_s 0.58009294 NA NA NA NA NA
## adona pfeesa nfdha x9cl_pf3ons x11cl_pf3o_ud_s
## pfba NA -0.46432978 NA -0.015998696 NA
## pf_pe_a 0.8264868 -0.38324818 NA 0.049480936 0.6159388
## pf_hx_a -0.4663214 0.16602435 NA -0.006153293 0.2286880
## pf_hp_a 0.7942774 -0.08926641 NA 0.027683653 0.7076887
## pfoa 0.9842393 -0.23027405 NA 0.103276819 0.2136916
## pfna 0.8007973 -0.14879045 NA 0.162233086 0.4100189
## pfda 0.9948935 -0.27050966 NA 0.358111251 0.4920543
## pf_un_a 0.9973267 -0.27097015 NA 0.347484941 0.4262741
## pf_do_a 0.8427310 -0.10072171 NA 0.184391389 0.3409388
## hfpo_da NA NA NA -0.036500205 NA
## pfbs 0.9992601 0.18049341 NA -0.009044973 0.7578964
## pf_pe_s 0.9607689 -0.18093476 NA 0.179528111 0.7433419
## pf_hx_s 0.9962564 -0.25296145 NA 0.217823779 0.4019372
## pf_hp_s 0.6615900 -0.31630560 NA 0.180519256 0.4973183
## pfos 0.5938991 -0.31206574 NA 0.271958503 0.5800929
## x4_2fts NA NA NA NA NA
## x6_2fts NA NA NA -0.064018440 NA
## x8_2fts NA NA NA -0.235970171 NA
## pfmpa NA NA NA NA NA
## pfmba NA NA NA NA NA
## adona 1.0000000 NA NA -1.000000000 NA
## pfeesa NA 1.00000000 NA -0.002011390 NA
## nfdha NA NA NA NA NA
## x9cl_pf3ons -1.0000000 -0.00201139 NA 1.000000000 0.9936712
## x11cl_pf3o_ud_s NA NA NA 0.993671174 1.0000000correlation_long <- melt(correlation_matrix)
heatmap_plot <- ggplot(data = correlation_long, aes(Var1, Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name="Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1)) +
coord_fixed() +
labs(x = "PFAS", y = "PFAS", title = "Correlation Heatmap of PFAS Variables")
print(heatmap_plot)
##scaled 14 PFAS
data_pfas1<- data %>% dplyr::select(contains("scld"))
correlation_matrix1 <- data_pfas1 %>%
select_if(~sum(!is.na(.)) > 0) %>%
cor(use = "pairwise.complete.obs")
print(correlation_matrix1)## pf_hx_s_scld pfda_scld pfna_scld pfos_scld pf_hp_a_scld
## pf_hx_s_scld 1.00000000 0.215283880 0.29610640 0.46909749 0.29696176
## pfda_scld 0.21528388 1.000000000 0.79198002 0.75799836 0.18264936
## pfna_scld 0.29610640 0.791980024 1.00000000 0.61107072 0.19186209
## pfos_scld 0.46909749 0.757998359 0.61107072 1.00000000 0.17686502
## pf_hp_a_scld 0.29696176 0.182649363 0.19186209 0.17686502 1.00000000
## pfbs_scld 0.05422040 0.073781847 0.04671063 0.07154140 0.48634102
## pfoa_scld 0.53757395 0.531275533 0.76335319 0.44506674 0.33935398
## pf_pe_a_scld -0.08103830 0.004784158 -0.05984060 -0.02353600 -0.05439341
## pf_un_a_scld 0.21825075 0.818745228 0.56175956 0.71040513 0.13481229
## pf_hp_s_scld 0.70293011 0.534993490 0.56589836 0.86330712 0.22368108
## pf_do_a_scld 0.10232345 0.724313889 0.40465597 0.62226092 0.14068137
## pf_pe_s_scld 0.67295348 0.221581293 0.19659482 0.31946623 0.47944494
## pf_hx_a_scld -0.07178676 -0.115760252 -0.10604061 -0.13001298 0.14482447
## pfba_scld -0.03232960 0.011123974 -0.06684597 -0.01592241 0.25003054
## pfbs_scld pfoa_scld pf_pe_a_scld pf_un_a_scld pf_hp_s_scld
## pf_hx_s_scld 0.05422040 0.53757395 -0.0810383044 0.2182507466 0.70293011
## pfda_scld 0.07378185 0.53127553 0.0047841577 0.8187452278 0.53499349
## pfna_scld 0.04671063 0.76335319 -0.0598405991 0.5617595563 0.56589836
## pfos_scld 0.07154140 0.44506674 -0.0235359965 0.7104051311 0.86330712
## pf_hp_a_scld 0.48634102 0.33935398 -0.0543934086 0.1348122875 0.22368108
## pfbs_scld 1.00000000 0.07880220 -0.0300843635 0.0898649439 0.06501304
## pfoa_scld 0.07880220 1.00000000 -0.0798103861 0.3277988109 0.55092467
## pf_pe_a_scld -0.03008436 -0.07981039 1.0000000000 -0.0002736169 -0.07916308
## pf_un_a_scld 0.08986494 0.32779881 -0.0002736169 1.0000000000 0.48422199
## pf_hp_s_scld 0.06501304 0.55092467 -0.0791630751 0.4842219935 1.00000000
## pf_do_a_scld 0.12501711 0.22460958 0.0080579055 0.7608520307 0.36425369
## pf_pe_s_scld 0.13507007 0.47473027 -0.0568650696 0.2237770503 0.45729373
## pf_hx_a_scld 0.01192492 -0.09012840 0.0504638340 -0.1402036384 -0.14270559
## pfba_scld 0.21265617 -0.05723167 0.0543499400 0.0215084011 -0.03841597
## pf_do_a_scld pf_pe_s_scld pf_hx_a_scld pfba_scld
## pf_hx_s_scld 0.102323446 0.672953480 -0.07178676 -0.032329599
## pfda_scld 0.724313889 0.221581293 -0.11576025 0.011123974
## pfna_scld 0.404655974 0.196594819 -0.10604061 -0.066845973
## pfos_scld 0.622260918 0.319466233 -0.13001298 -0.015922406
## pf_hp_a_scld 0.140681373 0.479444944 0.14482447 0.250030540
## pfbs_scld 0.125017110 0.135070072 0.01192492 0.212656172
## pfoa_scld 0.224609575 0.474730274 -0.09012840 -0.057231673
## pf_pe_a_scld 0.008057905 -0.056865070 0.05046383 0.054349940
## pf_un_a_scld 0.760852031 0.223777050 -0.14020364 0.021508401
## pf_hp_s_scld 0.364253692 0.457293728 -0.14270559 -0.038415970
## pf_do_a_scld 1.000000000 0.174840064 -0.09093638 0.092717740
## pf_pe_s_scld 0.174840064 1.000000000 -0.01020189 -0.001061104
## pf_hx_a_scld -0.090936382 -0.010201890 1.00000000 -0.050386280
## pfba_scld 0.092717740 -0.001061104 -0.05038628 1.000000000correlation_long1 <- melt(correlation_matrix1)
heatmap_plot1 <- ggplot(data = correlation_long1, aes(Var1, Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name="Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1)) +
coord_fixed() +
labs(x = "PFAS", y = "PFAS", title = "Correlation Heatmap of Scaled PFAS Variables")
print(heatmap_plot1)
Descriptive stats of PFAS in overall paricipants
data_pfas<- data %>% dplyr::select(pfas_name_analysis)
## Stats----
(pfas_stats <- data_pfas %>%
pivot_longer(cols = pfas_name_analysis) %>%
drop_na() %>%
group_by(name) %>%
dplyr::summarise(
geometric_mean = jag2::fungm(value),
sd = jag2::fungsd(value),
min = min(value),
max = max(value),
percentile_25 = jag2::qntle_fxn(value, 0.25),
percentile_50 = jag2::qntle_fxn(value, 0.5),
percentile_75 = jag2::qntle_fxn(value, 0.75),
percentile_90 = jag2::qntle_fxn(value, 0.90)
) %>%
ungroup())#writexl::write_xlsx(pfas_stats,
# fs::path(dir_result,
# "descriptive_stats_pfas.xlsx"))Process of selecting outcome variables
#1. ALT
num_na_alt <- sum(is.na(data$alt_u_l))
cat("Print: the number of NA in ALT=", num_na_alt)## Print: the number of NA in ALT= 29cat("Print: percentage of NA in ALT=", (num_na_alt/dim(data)[1])*100)## Print: percentage of NA in ALT= 7.671958# 1-1. check the distribution of ALT
alt_clean <- na.omit(data$alt_u_l) # Remove missing values
min_alt <- min(alt_clean)# Calculate range
max_alt <- max(alt_clean)
mean_alt <- mean(alt_clean)# Calculate mean
sd_alt <- sd(alt_clean)# Calculate standard deviation
(data %>% dplyr::select(pfas_name_analysis, covars,race_final_label,
ethnicity,contains("sq_"),contains("alt"),
contains("ast"), contains("trig"),cirrhosis,
case_control) %>%
tbl_summary(by = case_control,
type = all_continuous() ~ "continuous2",
statistic = list(
all_continuous() ~ c("{mean} ({sd})",
"{median} ({p25}, {p75})"),
all_categorical() ~ "{n} ({p}%)")) %>%
add_p(pvalue_fun = ~ style_pvalue(.x, digits = 2)) %>%
add_overall() %>%
add_n())## 35 observations missing `case_control` have been removed. To include these observations, use `forcats::fct_na_value_to_level()` on `case_control` column before passing to `tbl_summary()`.## There was an error in 'add_p()/add_difference()' for variable 'alt_normal_range', p-value omitted:
## Error in stats::chisq.test(x = c("7 - 55 U/L", "7 - 55 U/L", "7 - 55 U/L", : 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()/add_difference()' for variable 'ast_normal_range', p-value omitted:
## Error in stats::chisq.test(x = c("5 - 34 U/L", "5 - 34 U/L", "5 - 34 U/L", : 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()/add_difference()' for variable 'trig_normal_range', p-value omitted:
## Error in stats::chisq.test(x = c("0 - 149 mg/dL", "0 - 149 mg/dL", "0 - 149 mg/dL", : 'x' and 'y' must have at least 2 levels| Characteristic | N | Overall, N = 3431 | Healthy, N = 2061 | With cirrhosis, N = 1371 | p-value2 |
|---|---|---|---|---|---|
| pf_hx_s | 343 | 0.003 | |||
| Â Â Â Â Mean (SD) | 1.46 (1.60) | 1.61 (1.71) | 1.23 (1.39) | ||
| Â Â Â Â Median (IQR) | 1.05 (0.54, 1.74) | 1.21 (0.61, 1.91) | 0.86 (0.44, 1.46) | ||
| pfda | 343 | <0.001 | |||
| Â Â Â Â Mean (SD) | 0.26 (0.33) | 0.29 (0.35) | 0.21 (0.31) | ||
| Â Â Â Â Median (IQR) | 0.16 (0.09, 0.27) | 0.18 (0.11, 0.30) | 0.12 (0.07, 0.23) | ||
| pfna | 343 | <0.001 | |||
| Â Â Â Â Mean (SD) | 0.55 (0.54) | 0.62 (0.58) | 0.45 (0.45) | ||
| Â Â Â Â Median (IQR) | 0.42 (0.25, 0.65) | 0.48 (0.32, 0.73) | 0.34 (0.18, 0.54) | ||
| pfos | 343 | <0.001 | |||
| Â Â Â Â Mean (SD) | 6.1 (6.5) | 6.8 (6.5) | 5.1 (6.5) | ||
| Â Â Â Â Median (IQR) | 4.2 (2.3, 7.1) | 4.8 (2.9, 8.6) | 3.4 (1.7, 6.1) | ||
| pf_hp_a | 343 | 0.020 | |||
| Â Â Â Â Mean (SD) | 0.07 (0.05) | 0.06 (0.04) | 0.08 (0.07) | ||
| Â Â Â Â Median (IQR) | 0.05 (0.03, 0.08) | 0.05 (0.03, 0.07) | 0.05 (0.03, 0.10) | ||
| pfbs | 343 | 0.94 | |||
| Â Â Â Â Mean (SD) | 0.050 (0.110) | 0.044 (0.082) | 0.057 (0.142) | ||
| Â Â Â Â Median (IQR) | 0.029 (0.020, 0.047) | 0.028 (0.020, 0.044) | 0.029 (0.018, 0.049) | ||
| pfoa | 343 | <0.001 | |||
| Â Â Â Â Mean (SD) | 1.44 (1.47) | 1.63 (1.67) | 1.16 (1.03) | ||
| Â Â Â Â Median (IQR) | 1.08 (0.63, 1.85) | 1.21 (0.75, 2.03) | 0.91 (0.48, 1.52) | ||
| pf_pe_a | 343 | 0.023 | |||
| Â Â Â Â Mean (SD) | 0.049 (0.032) | 0.051 (0.036) | 0.046 (0.025) | ||
| Â Â Â Â Median (IQR) | 0.042 (0.034, 0.053) | 0.043 (0.036, 0.054) | 0.037 (0.032, 0.052) | ||
| pf_un_a | 343 | <0.001 | |||
| Â Â Â Â Mean (SD) | 0.13 (0.15) | 0.15 (0.15) | 0.09 (0.14) | ||
| Â Â Â Â Median (IQR) | 0.08 (0.04, 0.15) | 0.10 (0.05, 0.19) | 0.06 (0.03, 0.10) | ||
| pf_hp_s | 343 | <0.001 | |||
| Â Â Â Â Mean (SD) | 0.20 (0.17) | 0.22 (0.17) | 0.17 (0.16) | ||
| Â Â Â Â Median (IQR) | 0.15 (0.08, 0.27) | 0.16 (0.10, 0.29) | 0.13 (0.06, 0.23) | ||
| pf_do_a | 343 | 0.001 | |||
| Â Â Â Â Mean (SD) | 0.021 (0.035) | 0.023 (0.033) | 0.019 (0.038) | ||
| Â Â Â Â Median (IQR) | 0.012 (0.007, 0.021) | 0.014 (0.007, 0.024) | 0.009 (0.005, 0.016) | ||
| pf_pe_s | 343 | 0.036 | |||
| Â Â Â Â Mean (SD) | 0.028 (0.031) | 0.030 (0.035) | 0.024 (0.024) | ||
| Â Â Â Â Median (IQR) | 0.019 (0.011, 0.033) | 0.020 (0.013, 0.036) | 0.018 (0.010, 0.030) | ||
| pf_hx_a | 343 | 0.004 | |||
| Â Â Â Â Mean (SD) | 0.011 (0.011) | 0.010 (0.010) | 0.013 (0.012) | ||
| Â Â Â Â Median (IQR) | 0.010 (0.002, 0.014) | 0.009 (0.002, 0.013) | 0.011 (0.002, 0.017) | ||
| pfba | 343 | 0.092 | |||
| Â Â Â Â Mean (SD) | 0.03 (0.07) | 0.02 (0.05) | 0.04 (0.09) | ||
| Â Â Â Â Median (IQR) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | ||
| source | 343 | <0.001 | |||
| Â Â Â Â DUKE | 86 (25%) | 7 (3.4%) | 79 (58%) | ||
| Â Â Â Â Emory | 92 (27%) | 54 (26%) | 38 (28%) | ||
| Â Â Â Â NCSU | 147 (43%) | 145 (70%) | 2 (1.5%) | ||
| Â Â Â Â UNC | 18 (5.2%) | 0 (0%) | 18 (13%) | ||
| age_at_enrollment | 331 | 0.29 | |||
| Â Â Â Â Mean (SD) | 59 (10) | 59 (10) | 60 (8) | ||
| Â Â Â Â Median (IQR) | 59 (52, 67) | 59 (51, 68) | 60 (55, 67) | ||
| Â Â Â Â Unknown | 12 | 6 | 6 | ||
| sex | 343 | <0.001 | |||
| Â Â Â Â Female | 219 (64%) | 148 (72%) | 71 (52%) | ||
| Â Â Â Â Male | 124 (36%) | 58 (28%) | 66 (48%) | ||
| race_eth_label | 343 | <0.001 | |||
| Â Â Â Â Hispanic | 12 (3.5%) | 6 (2.9%) | 6 (4.4%) | ||
| Â Â Â Â NHB | 94 (27%) | 76 (37%) | 18 (13%) | ||
| Â Â Â Â NHO | 15 (4.4%) | 10 (4.9%) | 5 (3.6%) | ||
| Â Â Â Â NHW | 208 (61%) | 105 (51%) | 103 (75%) | ||
| Â Â Â Â Unknown/Not Reported | 14 (4.1%) | 9 (4.4%) | 5 (3.6%) | ||
| race_final_label | 343 | <0.001 | |||
| Â Â Â Â American Indian | 2 (0.6%) | 0 (0%) | 2 (1.5%) | ||
| Â Â Â Â American Indian/Alaskan Native | 4 (1.2%) | 4 (1.9%) | 0 (0%) | ||
| Â Â Â Â Asian | 3 (0.9%) | 1 (0.5%) | 2 (1.5%) | ||
| Â Â Â Â Asian/Pacific Islander | 6 (1.7%) | 5 (2.4%) | 1 (0.7%) | ||
| Â Â Â Â Black | 98 (29%) | 80 (39%) | 18 (13%) | ||
| Â Â Â Â Other | 7 (2.0%) | 6 (2.9%) | 1 (0.7%) | ||
| Â Â Â Â White | 223 (65%) | 110 (53%) | 113 (82%) | ||
| ethnicity | 343 | 0.81 | |||
| Â Â Â Â Hispanic | 12 (3.5%) | 6 (2.9%) | 6 (4.4%) | ||
| Â Â Â Â Not Hispanic | 317 (92%) | 191 (93%) | 126 (92%) | ||
| Â Â Â Â Unknown/Not Reported | 14 (4.1%) | 9 (4.4%) | 5 (3.6%) | ||
| rural | 343 | <0.001 | |||
| Â Â Â Â Living in Metro area | 272 (79%) | 184 (89%) | 88 (64%) | ||
| Â Â Â Â Living in rural area | 31 (9.0%) | 10 (4.9%) | 21 (15%) | ||
| Â Â Â Â Unknown/Not Reported | 40 (12%) | 12 (5.8%) | 28 (20%) | ||
| smoking | 343 | <0.001 | |||
| Â Â Â Â Don't smoke or use vape | 236 (69%) | 157 (76%) | 79 (58%) | ||
| Â Â Â Â Smoke or use vape | 35 (10%) | 20 (9.7%) | 15 (11%) | ||
| Â Â Â Â Unknown/Not Reported | 72 (21%) | 29 (14%) | 43 (31%) | ||
| sq_drink_alcohol | 343 | <0.001 | |||
| Â Â Â Â No, former drinker (stopped) | 78 (23%) | 26 (13%) | 52 (38%) | ||
| Â Â Â Â No, never drinker | 84 (24%) | 49 (24%) | 35 (26%) | ||
| Â Â Â Â Unknown/Not Reported | 73 (21%) | 30 (15%) | 43 (31%) | ||
| Â Â Â Â Yes, current drinker | 108 (31%) | 101 (49%) | 7 (5.1%) | ||
| sq_average_drink_per_day | 343 | <0.001 | |||
| Â Â Â Â 1-2 alcoholic drinks per day | 18 (5.2%) | 17 (8.3%) | 1 (0.7%) | ||
| Â Â Â Â 3-4 alcoholic drinks per day | 6 (1.7%) | 5 (2.4%) | 1 (0.7%) | ||
| Â Â Â Â Less than 1 alcoholic drink per day | 84 (24%) | 79 (38%) | 5 (3.6%) | ||
| Â Â Â Â Unknown/Not Reported | 235 (69%) | 105 (51%) | 130 (95%) | ||
| sq_self_hep_b | 343 | <0.001 | |||
| Â Â Â Â No | 248 (72%) | 165 (80%) | 83 (61%) | ||
| Â Â Â Â Unknown/Not Reported | 78 (23%) | 32 (16%) | 46 (34%) | ||
| Â Â Â Â Yes | 17 (5.0%) | 9 (4.4%) | 8 (5.8%) | ||
| sq_self_hep_c | 343 | <0.001 | |||
| Â Â Â Â No | 248 (72%) | 173 (84%) | 75 (55%) | ||
| Â Â Â Â Unknown/Not Reported | 77 (22%) | 31 (15%) | 46 (34%) | ||
| Â Â Â Â Yes | 18 (5.2%) | 2 (1.0%) | 16 (12%) | ||
| supp_meds_tylenol | 343 | 0.012 | |||
| Â Â Â Â No | 7 (2.0%) | 7 (3.4%) | 0 (0%) | ||
| Â Â Â Â Unknown/Not Reported | 332 (97%) | 195 (95%) | 137 (100%) | ||
| Â Â Â Â Yes | 4 (1.2%) | 4 (1.9%) | 0 (0%) | ||
| supp_meds_steroids | 343 | 0.013 | |||
| Â Â Â Â No | 9 (2.6%) | 9 (4.4%) | 0 (0%) | ||
| Â Â Â Â Unknown/Not Reported | 333 (97%) | 196 (95%) | 137 (100%) | ||
| Â Â Â Â Yes | 1 (0.3%) | 1 (0.5%) | 0 (0%) | ||
| sq_water_well | 343 | 0.004 | |||
| Â Â Â Â No | 179 (52%) | 120 (58%) | 59 (43%) | ||
| Â Â Â Â Unknown/Not Reported | 108 (31%) | 62 (30%) | 46 (34%) | ||
| Â Â Â Â Yes | 56 (16%) | 24 (12%) | 32 (23%) | ||
| sq_water_tap_unfiltered | 343 | 0.064 | |||
| Â Â Â Â No | 85 (25%) | 48 (23%) | 37 (27%) | ||
| Â Â Â Â Unknown/Not Reported | 92 (27%) | 48 (23%) | 44 (32%) | ||
| Â Â Â Â Yes | 166 (48%) | 110 (53%) | 56 (41%) | ||
| sq_water_house_filtration | 343 | 0.98 | |||
| Â Â Â Â No | 195 (57%) | 118 (57%) | 77 (56%) | ||
| Â Â Â Â Unknown/Not Reported | 113 (33%) | 67 (33%) | 46 (34%) | ||
| Â Â Â Â Yes | 35 (10%) | 21 (10%) | 14 (10%) | ||
| sq_water_faucet_filter | 343 | 0.60 | |||
| Â Â Â Â No | 133 (39%) | 81 (39%) | 52 (38%) | ||
| Â Â Â Â Unknown/Not Reported | 105 (31%) | 59 (29%) | 46 (34%) | ||
| Â Â Â Â Yes | 105 (31%) | 66 (32%) | 39 (28%) | ||
| sq_water_charcoal_filter | 343 | 0.90 | |||
| Â Â Â Â No | 178 (52%) | 109 (53%) | 69 (50%) | ||
| Â Â Â Â Unknown/Not Reported | 112 (33%) | 66 (32%) | 46 (34%) | ||
| Â Â Â Â Yes | 53 (15%) | 31 (15%) | 22 (16%) | ||
| sq_water_bottled | 343 | 0.25 | |||
| Â Â Â Â No | 80 (23%) | 54 (26%) | 26 (19%) | ||
| Â Â Â Â Unknown/Not Reported | 99 (29%) | 55 (27%) | 44 (32%) | ||
| Â Â Â Â Yes | 164 (48%) | 97 (47%) | 67 (49%) | ||
| sq_water_none | 343 | 0.048 | |||
| Â Â Â Â No | 210 (61%) | 132 (64%) | 78 (57%) | ||
| Â Â Â Â Unknown/Not Reported | 115 (34%) | 68 (33%) | 47 (34%) | ||
| Â Â Â Â Yes | 18 (5.2%) | 6 (2.9%) | 12 (8.8%) | ||
| sq_water_other_type | 343 | 0.61 | |||
| Â Â Â Â No | 206 (60%) | 128 (62%) | 78 (57%) | ||
| Â Â Â Â Unknown/Not Reported | 110 (32%) | 62 (30%) | 48 (35%) | ||
| Â Â Â Â Yes | 27 (7.9%) | 16 (7.8%) | 11 (8.0%) | ||
| sq_water_dont_know | 343 | 0.79 | |||
| Â Â Â Â No | 210 (61%) | 129 (63%) | 81 (59%) | ||
| Â Â Â Â Unknown/Not Reported | 115 (34%) | 67 (33%) | 48 (35%) | ||
| Â Â Â Â Yes | 18 (5.2%) | 10 (4.9%) | 8 (5.8%) | ||
| alt_u_l | 316 | <0.001 | |||
| Â Â Â Â Mean (SD) | 16 (15) | 15 (17) | 17 (12) | ||
| Â Â Â Â Median (IQR) | 12 (8, 19) | 11 (8, 16) | 15 (10, 21) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| log_alt | 316 | <0.001 | |||
| Â Â Â Â Mean (SD) | 2.52 (0.65) | 2.44 (0.65) | 2.65 (0.63) | ||
| Â Â Â Â Median (IQR) | 2.48 (2.08, 2.94) | 2.40 (2.08, 2.77) | 2.71 (2.30, 3.04) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| alt_normal_range | 316 | ||||
| Â Â Â Â 7 - 55 U/L | 316 (100%) | 187 (100%) | 129 (100%) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| alt_flags | 316 | 0.44 | |||
| Â Â Â Â < | 19 (6.0%) | 12 (6.4%) | 7 (5.4%) | ||
| Â Â Â Â HIGH | 8 (2.5%) | 6 (3.2%) | 2 (1.6%) | ||
| Â Â Â Â LOW | 16 (5.1%) | 12 (6.4%) | 4 (3.1%) | ||
| Â Â Â Â Norm | 273 (86%) | 157 (84%) | 116 (90%) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| AST/ALT | 316 | 0.002 | |||
| Â Â Â Â Mean (SD) | 2.82 (1.93) | 2.59 (1.59) | 3.15 (2.30) | ||
| Â Â Â Â Median (IQR) | 2.29 (1.77, 3.16) | 2.18 (1.73, 2.86) | 2.44 (2.00, 3.50) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| alt_cat1 | 316 | 0.062 | |||
| Â Â Â Â High | 47 (15%) | 22 (12%) | 25 (19%) | ||
| Â Â Â Â Norm | 269 (85%) | 165 (88%) | 104 (81%) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| alt_cat2 | 316 | 0.30 | |||
| Â Â Â Â High | 28 (8.9%) | 14 (7.5%) | 14 (11%) | ||
| Â Â Â Â Norm | 288 (91%) | 173 (93%) | 115 (89%) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| ast_u_l | 316 | <0.001 | |||
| Â Â Â Â Mean (SD) | 38 (32) | 33 (31) | 45 (33) | ||
| Â Â Â Â Median (IQR) | 27 (20, 42) | 21 (18, 32) | 36 (26, 49) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| log_ast | 316 | <0.001 | |||
| Â Â Â Â Mean (SD) | 3.41 (0.61) | 3.25 (0.59) | 3.64 (0.57) | ||
| Â Â Â Â Median (IQR) | 3.28 (3.00, 3.74) | 3.04 (2.89, 3.47) | 3.58 (3.26, 3.89) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| ast_normal_range | 316 | ||||
| Â Â Â Â 5 - 34 U/L | 316 (100%) | 187 (100%) | 129 (100%) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| ast_flags | 316 | <0.001 | |||
| Â Â Â Â HIGH | 113 (36%) | 44 (24%) | 69 (53%) | ||
| Â Â Â Â Norm | 203 (64%) | 143 (76%) | 60 (47%) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| ast_cat1 | 316 | <0.001 | |||
| Â Â Â Â High | 203 (64%) | 102 (55%) | 101 (78%) | ||
| Â Â Â Â Norm | 113 (36%) | 85 (45%) | 28 (22%) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| ast_cat2 | 316 | <0.001 | |||
| Â Â Â Â High | 157 (50%) | 67 (36%) | 90 (70%) | ||
| Â Â Â Â Norm | 159 (50%) | 120 (64%) | 39 (30%) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| trig_mg_d_l | 322 | 0.037 | |||
| Â Â Â Â Mean (SD) | 135 (79) | 143 (83) | 123 (71) | ||
| Â Â Â Â Median (IQR) | 112 (79, 165) | 117 (82, 176) | 105 (77, 154) | ||
| Â Â Â Â Unknown | 21 | 15 | 6 | ||
| log_trig | 322 | 0.037 | |||
| Â Â Â Â Mean (SD) | 4.76 (0.54) | 4.82 (0.52) | 4.67 (0.55) | ||
| Â Â Â Â Median (IQR) | 4.72 (4.37, 5.10) | 4.76 (4.40, 5.17) | 4.65 (4.34, 5.03) | ||
| Â Â Â Â Unknown | 21 | 15 | 6 | ||
| trig_normal_range | 322 | ||||
| Â Â Â Â 0 - 149 mg/dL | 322 (100%) | 191 (100%) | 131 (100%) | ||
| Â Â Â Â Unknown | 21 | 15 | 6 | ||
| trig_flags | 322 | 0.093 | |||
| Â Â Â Â HIGH | 103 (32%) | 68 (36%) | 35 (27%) | ||
| Â Â Â Â Norm | 219 (68%) | 123 (64%) | 96 (73%) | ||
| Â Â Â Â Unknown | 21 | 15 | 6 | ||
| cirrhosis | 316 | 0.15 | |||
| Â Â Â Â Healthy | 8 (2.5%) | 7 (3.7%) | 1 (0.8%) | ||
| Â Â Â Â With cirrhosis | 308 (97%) | 180 (96%) | 128 (99%) | ||
| Â Â Â Â Unknown | 27 | 19 | 8 | ||
| 1 n (%) | |||||
| 2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test | |||||
Correlation between PFAS and potential confounders
covars <- c("source", "age_at_enrollment","sex",
"race_eth_label", "race_final_label","ethnicity",
"rural", "smoking","sq_drink_alcohol","sq_average_drink_per_day","sq_self_hep_b",
"sq_self_hep_c","supp_meds_tylenol","supp_meds_steroids","sq_water_well",
"sq_water_tap_unfiltered","sq_water_house_filtration", "sq_water_faucet_filter",
"sq_water_charcoal_filter","sq_water_bottled","sq_water_none","sq_water_other_type")
cat("Print: range of ALT=", min_alt," and ",max_alt, "\n")## Print: range of ALT= 3 and 186cat("Print: mean and SD of ALT=", mean_alt," and ", sd_alt, "\n")## Print: mean and SD of ALT= 15.43266 and 14.65991# Redraw the plot with extended axis limits
plotNormalHistogram(alt_clean,
prob = FALSE,
col = "white",
border = "black",
main = "Distribution of ALT(u/l)",
xlab = "ALT(u/l)",
ylab = "Number of samples",
linecol = "red",
lwd = 3
)
density_data <- density(alt_clean)# Calculate density
hist_data <- hist(alt_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density
density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(alt_clean) # Scale density values to match the histogram frequency
# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)
# Add legend
legend("topright",
legend = c("Normal Distribution", "Density"),
col = c("red", "blue"),
lwd = 3,
cex = 0.8,
bty = "n")
# 1-2. check the distribution of ln(ALT), based on the distribution of ALT, which needs natural log transformation.
num_na_ln_alt <- sum(is.na(data$log_alt)) #check the na
cat("Print: the number of NA in Ln ALT=", num_na_ln_alt, "\n")## Print: the number of NA in Ln ALT= 29cat("Print: percentage of NA in Ln ALT=", (num_na_ln_alt/dim(data)[1])*100,"\n")## Print: percentage of NA in Ln ALT= 7.671958ln_alt_clean <- na.omit(data$log_alt) # Remove missing values
min_ln_alt <- min(ln_alt_clean)# Calculate range
max_ln_alt <- max(ln_alt_clean)
mean_ln_alt <- mean(ln_alt_clean)# Calculate mean
sd_ln_alt <- sd(ln_alt_clean)# Calculate standard deviation
cat("Print: range of ln ALT=", min_ln_alt,max_ln_alt, "\n")## Print: range of ln ALT= 1.098612 5.225747cat("Print: mean and SD of ln ALT=", mean_ln_alt," and ", sd_ln_alt, "\n")## Print: mean and SD of ln ALT= 2.514251 and 0.6316953plotNormalHistogram(ln_alt_clean, # Plot histogram with normal distribution line
prob = FALSE,
col = "white",
border = "black",
main = "Distribution of natural log ALT(u/l)",
xlab = "Natural log of ALT(u/l)",
ylab = "Number of samples",
linecol = "red",
lwd = 3)
density_data <- density(ln_alt_clean)# Calculate density
hist_data <- hist(ln_alt_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density
density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(ln_alt_clean) # Scale density values to match the histogram frequency
# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)
# Add legend
legend("topright",
legend = c("Normal Distribution", "Density"),
col = c("red", "blue"),
lwd = 3,
cex = 0.8,
bty = "n")
#2. AST
num_na_ast <- sum(is.na(data$ast_u_l))
cat("Print: the number of NA in AST=", num_na_ast)## Print: the number of NA in AST= 29cat("Print: percentage of NA in AST=", (num_na_ast/dim(data)[1])*100)## Print: percentage of NA in AST= 7.671958# 2-1. check the distribution of ast
ast_clean <- na.omit(data$ast_u_l) # Remove missing values
min_ast <- min(ast_clean)# Calculate range
max_ast <- max(ast_clean)
mean_ast <- mean(ast_clean)# Calculate mean
sd_ast <- sd(ast_clean)# Calculate standard deviation
cat("Print: range of AST=", min_ast," and ", max_ast, "\n")## Print: range of AST= 7 and 227cat("Print: mean and SD of AST=", mean_ast," and ", sd_ast, "\n")## Print: mean and SD of AST= 36.41261 and 30.77582# Redraw the plot with extended axis limits
plotNormalHistogram(ast_clean,
prob = FALSE,
col = "white",
border = "black",
main = "Distribution of AST(u/l)",
xlab = "AST(u/l)",
ylab = "Number of samples",
linecol = "red",
lwd = 3
)
density_data <- density(ast_clean)# Calculate density
hist_data <- hist(ast_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density
density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(ast_clean) # Scale density values to match the histogram frequency
# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)
# Add legend
legend("topright",
legend = c("Normal Distribution", "Density"),
col = c("red", "blue"),
lwd = 3,
cex = 0.8,
bty = "n")
# 2-2. check the distribution of ln(ast), based on the distribution of ast, which needs natural log transformation.
num_na_ln_ast <- sum(is.na(data$log_ast)) #check the na
cat("Print: the number of NA in Ln AST=", num_na_ln_ast, "\n")## Print: the number of NA in Ln AST= 29cat("Print: percentage of NA in Ln AST=", (num_na_ln_ast/dim(data)[1])*100,"\n")## Print: percentage of NA in Ln AST= 7.671958ln_ast_clean <- na.omit(data$log_ast) # Remove missing values
min_ln_ast <- min(ln_ast_clean)# Calculate range
max_ln_ast <- max(ln_ast_clean)
mean_ln_ast <- mean(ln_ast_clean)# Calculate mean
sd_ln_ast <- sd(ln_ast_clean)# Calculate standard deviation
cat("Print: range of ln AST=", min_ln_ast," and ", max_ln_ast, "\n")## Print: range of ln AST= 1.94591 and 5.42495cat("Print: mean and SD of ln AST=", mean_ln_ast," and ", sd_ln_ast, "\n")## Print: mean and SD of ln AST= 3.383119 and 0.5919342plotNormalHistogram(ln_ast_clean, # Plot histogram with normal distribution line
prob = FALSE,
col = "white",
border = "black",
main = "Distribution of natural log AST(u/l)",
xlab = "Natural log of AST(u/l)",
ylab = "Number of samples",
linecol = "red",
lwd = 3)
density_data <- density(ln_ast_clean)# Calculate density
hist_data <- hist(ln_ast_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density
density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(ln_ast_clean) # Scale density values to match the histogram frequency
# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)
# Add legend
legend("topright",
legend = c("Normal Distribution", "Density"),
col = c("red", "blue"),
lwd = 3,
cex = 0.8,
bty = "n")
#3. AST/ALT ratio
num_na_ast_alt_ratio <- sum(is.na(data$`AST/ALT`))
cat("Print: the number of NA in AST/ALT ratio=", num_na_ast_alt_ratio)## Print: the number of NA in AST/ALT ratio= 29cat("Print: percentage of NA in AST/ALT ratio=", (num_na_ast_alt_ratio/dim(data)[1])*100)## Print: percentage of NA in AST/ALT ratio= 7.671958#3-1. check the distribution of ast
ast_alt_clean <- na.omit(data$`AST/ALT`) # Remove missing values
min_ast_alt <- min(ast_alt_clean)# Calculate range
max_ast_alt <- max(ast_alt_clean)
mean_ast_alt <- mean(ast_alt_clean)# Calculate mean
sd_ast_alt <- sd(ast_alt_clean)# Calculate standard deviation
cat("Print: range of AST/ALT ratio=", min_ast_alt," and ", max_ast_alt, "\n")## Print: range of AST/ALT ratio= 0.6236559 and 16cat("Print: mean and SD of AST/ALT ratio=", mean_ast_alt," and ", sd_ast_alt, "\n")## Print: mean and SD of AST/ALT ratio= 2.744878 and 1.856091# Redraw the plot with extended axis limits
plotNormalHistogram(ast_alt_clean,
prob = FALSE,
col = "white",
border = "black",
main = "Distribution of AST/ALT ratio",
xlab = "AST/ALT ratio",
ylab = "Number of samples",
linecol = "red",
lwd = 3
)
density_data <- density(ast_alt_clean)# Calculate density
hist_data <- hist(ast_alt_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density
density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(ast_alt_clean) # Scale density values to match the histogram frequency
# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)
# Add legend
legend("topright",
legend = c("Normal Distribution", "Density"),
col = c("red", "blue"),
lwd = 3,
cex = 0.8,
bty = "n")
# 3-2. check the distribution of ln(ast/alt), based on the distribution of ast/alt ratio, which needs natural log transformation.
num_na_ln_ast_alt <- sum(is.na(data$log_ast_alt)) #check the na
cat("Print: the number of NA in Ln AST/ALT=", num_na_ln_ast_alt, "\n")## Print: the number of NA in Ln AST/ALT= 29cat("Print: percentage of NA in Ln AST/ALT=", (num_na_ln_ast_alt/dim(data)[1])*100,"\n")## Print: percentage of NA in Ln AST/ALT= 7.671958ln_ast_alt_clean <- na.omit(data$log_ast_alt) # Remove missing values
min_ln_ast_alt <- min(ln_ast_alt_clean)# Calculate range
max_ln_ast_alt <- max(ln_ast_alt_clean)
mean_ln_ast_alt <- mean(ln_ast_alt_clean)# Calculate mean
sd_ln_ast_alt <- sd(ln_ast_alt_clean)# Calculate standard deviation
cat("Print: range of ln AST/ALT=", min_ln_ast,max_ln_ast_alt, "\n")## Print: range of ln AST/ALT= 1.94591 2.772589cat("Print: mean and SD of ln AST/ALT=", mean_ln_ast," and ", sd_ln_ast_alt, "\n")## Print: mean and SD of ln AST/ALT= 3.383119 and 0.4937588plotNormalHistogram(ln_ast_alt_clean, # Plot histogram with normal distribution line
prob = FALSE,
col = "white",
border = "black",
main = "Distribution of natural log AST/ALT ratio",
xlab = "Natural log of AST/ALT ratio",
ylab = "Number of samples",
linecol = "red",
lwd = 3)
density_data <- density(ln_ast_alt_clean)# Calculate density
hist_data <- hist(ln_ast_alt_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density
density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(ln_ast_alt_clean) # Scale density values to match the histogram frequency
# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)
# Add legend
legend("topright",
legend = c("Normal Distribution", "Density"),
col = c("red", "blue"),
lwd = 3,
cex = 0.8,
bty = "n")
# 4. correlation between natural ln ast, ln alt, and ln ast/alt ratio by sex
# 4-1. version 1
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'sex')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'sex')
custom_plot <- function(data, mapping, ...) {#Define the custom plot function for the lower panel
ggplot(data, mapping) +
geom_point(aes(color = sex), ...) +
geom_smooth(aes(color = sex), method = "lm", se = FALSE, ...) # Separate regression lines by sex
}
p <- ggpairs(data_renamed,# Generate the ggpairs plot with the custom plot function
columns = 1:3, # Only use the numeric columns for the pairs plot
aes(color = sex), # Color points by sex
lower = list(continuous = custom_plot),
upper = list(continuous = wrap("cor", size = 4))) +
theme_bw()
print(p)## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
#4-2. version2
custom_plot <- function(data, mapping, ...) {
ggplot(data, mapping) +
geom_point(aes(color = sex), ...) +
geom_smooth(aes(color = sex), method = "lm", se = FALSE, ...)
}
p <- ggpairs(data_renamed,
aes(color = sex),
lower = list(continuous = custom_plot),
upper = list(continuous = wrap("cor", size = 4))) +
theme_bw()
for(i in 1:p$nrow) {# Customize color scales manually
for(j in 1:p$ncol){
p[i,j] <- p[i,j] +
scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#00AFBB", "#FC4E07"))
}
}## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing valuesprint(p)## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
# 5. correlation between natural ln ast, ln alt, and ln ast/alt ratio by categorical endpoints
# 5-1. AST cat1: cutoff 29 for Male and 19 for female
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'ast_cat1')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'lowest cut AST')
custom_plot <- function(data, mapping, ...) {
ggplot(data, mapping) +
geom_point(aes(color = `lowest cut AST`), ...) +
geom_smooth(aes(color = `lowest cut AST`), method = "lm", se = FALSE, ...)
}
p1 <- ggpairs(data_renamed,
aes(color = `lowest cut AST`),
lower = list(continuous = custom_plot),
upper = list(continuous = wrap("cor", size = 4))) +
theme_bw()
for(i in 1:p$nrow) {# Customize color scales manually
for(j in 1:p$ncol){
p[i,j] <- p[i,j] +
scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#00AFBB", "#FC4E07"))
}
}## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.print(p1)## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
# 5-2. AST cat2: cutoff 33 for Male and 25 for female
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'ast_cat2')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'highest cut AST')
custom_plot <- function(data, mapping, ...) {
ggplot(data, mapping) +
geom_point(aes(color = `highest cut AST`), ...) +
geom_smooth(aes(color = `highest cut AST`), method = "lm", se = FALSE, ...)
}
p2 <- ggpairs(data_renamed,
aes(color = `highest cut AST`),
lower = list(continuous = custom_plot),
upper = list(continuous = wrap("cor", size = 4))) +
theme_bw()
for(i in 1:p$nrow) {# Customize color scales manually
for(j in 1:p$ncol){
p[i,j] <- p[i,j] +
scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#00AFBB", "#FC4E07"))
}
}## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.print(p2)## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
# 5-3. ALT cat1: cutoff 29 for Male and 19 for female
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'alt_cat1')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'lowest cut ALT')
custom_plot <- function(data, mapping, ...) {
ggplot(data, mapping) +
geom_point(aes(color = `lowest cut ALT`), ...) +
geom_smooth(aes(color = `lowest cut ALT`), method = "lm", se = FALSE, ...)
}
p3 <- ggpairs(data_renamed,
aes(color = `lowest cut ALT`),
lower = list(continuous = custom_plot),
upper = list(continuous = wrap("cor", size = 4))) +
theme_bw()
for(i in 1:p$nrow) {# Customize color scales manually
for(j in 1:p$ncol){
p[i,j] <- p[i,j] +
scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#00AFBB", "#FC4E07"))
}
}## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.print(p3)## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
# 5-4. ALT cat2: cutoff 33 for Male and 25 for female
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'alt_cat2')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'highest cut ALT')
custom_plot <- function(data, mapping, ...) {
ggplot(data, mapping) +
geom_point(aes(color = `highest cut ALT`), ...) +
geom_smooth(aes(color = `highest cut ALT`), method = "lm", se = FALSE, ...)
}
p4 <- ggpairs(data_renamed,
aes(color = `highest cut ALT`),
lower = list(continuous = custom_plot),
upper = list(continuous = wrap("cor", size = 4))) +
theme_bw()
for(i in 1:p$nrow) {# Customize color scales manually
for(j in 1:p$ncol){
p[i,j] <- p[i,j] +
scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#00AFBB", "#FC4E07"))
}
}## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.print(p4)## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
# 5-5. cirrhosis
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'cirrhosis')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'cirrhosis')
custom_plot <- function(data, mapping, ...) {
ggplot(data, mapping) +
geom_point(aes(color = `cirrhosis`), ...) +
geom_smooth(aes(color = `cirrhosis`), method = "lm", se = FALSE, ...)
}
p5 <- ggpairs(data_renamed,
aes(color = `cirrhosis`),
lower = list(continuous = custom_plot),
upper = list(continuous = wrap("cor", size = 4))) +
theme_bw()
for(i in 1:p$nrow) {# Customize color scales manually
for(j in 1:p$ncol){
p[i,j] <- p[i,j] +
scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#00AFBB", "#FC4E07"))
}
}## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.print(p5)## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
# 5-6. case_control
# 5-6-1. Total ppl
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'case_control')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'Cirrhosis diagnosed')
custom_plot <- function(data, mapping, ...) {
ggplot(data, mapping) +
geom_point(aes(color = `Cirrhosis diagnosed`), ...) +
geom_smooth(aes(color = `Cirrhosis diagnosed`), method = "lm", se = FALSE, ...)
}
p6 <- ggpairs(data_renamed,
aes(color = `Cirrhosis diagnosed`),
lower = list(continuous = custom_plot),
upper = list(continuous = wrap("cor", size = 4))) +
theme_bw()
for(i in 1:p$nrow) {# Customize color scales manually
for(j in 1:p$ncol){
p[i,j] <- p[i,j] +
scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#00AFBB", "#FC4E07"))
}
}## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.print(p6)## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
# 5-6-2. After excluding NCSU data
# Before analyzing this, subsetting excluding NCSU data (182 ppl, 48.1%)
#(dim(data[data$source=='NCSU',])[1]/dim(data)[1])*100
data_ex_ncsu <- data[data$source=='NCSU',]
data_renamed <- data_ex_ncsu[, c('log_ast_alt', 'log_ast', 'log_alt', 'case_control')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'Cirrhosis diagnosed')
# 6. A frequency table of categorical outcomes
# 6-1. Total ppl
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
frequency_tables <- lapply(data_renamed, function(x) {
tbl <- as.data.frame(table(x))
colnames(tbl) <- c("Category", "Frequency")
return(tbl)
})
combined_freq_df <- bind_rows(lapply(names(frequency_tables), function(name) {
df <- frequency_tables[[name]]
df$Variable <- name
return(df)
}))
combined_freq_df <- combined_freq_df %>%
pivot_wider(names_from = Variable, values_from = Frequency, values_fill = list(Frequency = 0)) %>%
arrange(Category)
combined_freq_df <- combined_freq_df %>%
dplyr::select(Category, everything())
combined_freq_df$Category <- as.character(combined_freq_df$Category)
total_row <- colSums(combined_freq_df[,-1])
total_row <- c("Total", total_row)
combined_freq_df <- rbind(combined_freq_df, total_row)
fancy_table1 <- flextable(combined_freq_df)
fancy_table1 <- set_table_properties(fancy_table1, width = 0.8, layout = "autofit")
fancy_table1 <- set_caption(fancy_table1, caption = "Frequency tables for categorical endpoints in total ppl (N=349)")
fancy_table1 <- fontsize(fancy_table1, size = 8, part = "body") # Reduce font size of values
fancy_table1 <- fontsize(fancy_table1, size = 9, part = "header") # Reduce font size of header
fancy_table1 <- border(fancy_table1, i = (nrow(fancy_table1$body$dataset)-1), border.bottom = fp_border(color = "black"))
fancy_table1 <- align(fancy_table1, align = "center", part = "body")
print(fancy_table1)## a flextable object.
## col_keys: `Category`, `lowest cut ast`, `highest cut ast`, `lowest cut alt`, `highest cut alt`, `cirrhosis`, `cirrhosis diagnosed`
## header has 1 row(s)
## body has 3 row(s)
## original dataset sample:
## Category lowest cut ast highest cut ast lowest cut alt highest cut alt
## 1 Abnormal 222 165 49 28
## 2 Normal 127 184 300 321
## 3 Total 349 349 349 349
## cirrhosis cirrhosis diagnosed
## 1 341 137
## 2 8 206
## 3 349 343# 6-2. After excluding Total ppl
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
frequency_tables <- lapply(data_renamed, function(x) {
tbl <- as.data.frame(table(x))
colnames(tbl) <- c("Category", "Frequency")
return(tbl)
})
combined_freq_df <- bind_rows(lapply(names(frequency_tables), function(name) {
df <- frequency_tables[[name]]
df$Variable <- name
return(df)
}))
combined_freq_df <- combined_freq_df %>%
pivot_wider(names_from = Variable, values_from = Frequency, values_fill = list(Frequency = 0)) %>%
arrange(Category)
combined_freq_df <- combined_freq_df %>%
dplyr::select(Category, everything())
combined_freq_df$Category <- as.character(combined_freq_df$Category)
total_row <- colSums(combined_freq_df[,-1])
total_row <- c("Total", total_row)
combined_freq_df <- rbind(combined_freq_df, total_row)
fancy_table2 <- flextable(combined_freq_df)
fancy_table2 <- set_table_properties(fancy_table2, width = 0.8, layout = "autofit")
fancy_table2 <- set_caption(fancy_table2, caption = "Frequency tables for categorical endpoints after exlcuding NCSU (N=173)")
fancy_table2 <- fontsize(fancy_table2, size = 8, part = "body") # Reduce font size of values
fancy_table2 <- fontsize(fancy_table2, size = 9, part = "header") # Reduce font size of header
fancy_table2 <- border(fancy_table2, i = (nrow(fancy_table2$body$dataset)-1), border.bottom = fp_border(color = "black"))
fancy_table2 <- align(fancy_table2, align = "center", part = "body")
print(fancy_table2)## a flextable object.
## col_keys: `Category`, `lowest cut ast`, `highest cut ast`, `lowest cut alt`, `highest cut alt`, `cirrhosis`, `cirrhosis diagnosed`
## header has 1 row(s)
## body has 3 row(s)
## original dataset sample:
## Category lowest cut ast highest cut ast lowest cut alt highest cut alt
## 1 Abnormal 88 43 10 5
## 2 Normal 85 130 163 168
## 3 Total 173 173 173 173
## cirrhosis cirrhosis diagnosed
## 1 170 2
## 2 3 145
## 3 173 147# Create a new Word document
doc <- read_docx()
# Add the first flextable
doc <- body_add_flextable(doc, value = fancy_table1)
# Add a page break (optional) or additional spacing
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")
# Add the second flextable
doc <- body_add_flextable(doc, value = fancy_table2)
# Save the document
print(doc, target = "Frequency tables of categorical outcome vars.docx")
# 7. Association between cat endpoints
# 7-1 through correlation matrix
# 7-1-1. total ppl
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
# Relabel the 'cirrhosis' variables
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
# Create dummy variables and compute correlation matrix
correlation_matrix <- model.matrix(~0 + ., data=data_renamed) %>%
cor(use="pairwise.complete.obs")
# Plot the correlation matrix using ggcorrplot
plot6 <-ggcorrplot(correlation_matrix,
show.diag=FALSE,
type="lower",
lab=TRUE,
lab_size=2,
title="Correlation matrix of categorical outcomes in total ppl (N=349)") + # Title of the plot
theme(
plot.title = element_text(size = 10), # Title font size
axis.text.x = element_text(size = 8), # X-axis title font size
axis.text.y = element_text(size = 8) # Y-axis title font size
)
print(plot6)
# 7-1-2. after excluding NCSU data
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
# Relabel the 'cirrhosis' variables
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
# Create dummy variables and compute correlation matrix
correlation_matrix <- model.matrix(~0 + ., data=data_renamed) %>%
cor(use="pairwise.complete.obs")
# Plot the correlation matrix using ggcorrplot
plot6 <-ggcorrplot(correlation_matrix,
show.diag=FALSE,
type="lower",
lab=TRUE,
lab_size=2,
title="Correlation matrix of categorical outcomes after excluding NCSU data (N=173)") + # Title of the plot
theme(
plot.title = element_text(size = 10), # Title font size
axis.text.x = element_text(size = 8), # X-axis title font size
axis.text.y = element_text(size = 8) # Y-axis title font size
)
print(plot6)
###########Tabulate
# 1. total ppl
# Create dummy variables
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
model_matrix <- model.matrix(~0 + ., data = data_renamed)
correlation_matrix <- cor(model_matrix, use = "pairwise.complete.obs")
# Function to calculate p-values for correlations
cor.mtest <- function(mat, ...) {
mat <- as.matrix(mat)
n <- ncol(mat)
p.mat <- matrix(NA, n, n)
diag(p.mat) <- 0
for (i in 1:(n - 1)) {
for (j in (i + 1):n) {
tmp <- cor.test(mat[, i], mat[, j], ...)
p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
}
}
colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
p.mat
}
# Calculate p-values
p.mat <- cor.mtest(model_matrix)
# Convert correlation matrix and p-value matrix to long format
cor_long <- melt(correlation_matrix)
pval_long <- melt(p.mat)
# Rename columns for merging
colnames(cor_long) <- c("Var1", "Var2", "correlation")
colnames(pval_long) <- c("Var1", "Var2", "pvalue")
# Merge correlation and p-value data frames
plot_df <- merge(cor_long, pval_long, by = c("Var1", "Var2"))
# Check the structure and content of the merged data frame
print("Structure of plot_df:")## [1] "Structure of plot_df:"str(plot_df)## 'data.frame': 49 obs. of 4 variables:
## $ Var1 : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 7 7 7 7 7 7 5 5 5 ...
## $ Var2 : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 5 3 4 1 2 6 7 5 3 ...
## $ correlation: num 1 0.0582 0.3336 0.1052 -0.2435 ...
## $ pvalue : num 0 0.30221339661 0.00000000118 0.06181134671 0.000011963 ...print("First few rows of plot_df:")## [1] "First few rows of plot_df:"head(plot_df)
# 5-6-1. Total ppl
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'case_control')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'Cirrhosis diagnosed')
custom_plot <- function(data, mapping, ...) {
ggplot(data, mapping) +
geom_point(aes(color = `Cirrhosis diagnosed`), ...) +
geom_smooth(aes(color = `Cirrhosis diagnosed`), method = "lm", se = FALSE, ...)
}
p6 <- ggpairs(data_renamed,
aes(color = `Cirrhosis diagnosed`),
lower = list(continuous = custom_plot),
upper = list(continuous = wrap("cor", size = 4))) +
theme_bw()
for(i in 1:p$nrow) {# Customize color scales manually
for(j in 1:p$ncol){
p[i,j] <- p[i,j] +
scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
scale_color_manual(values=c("#00AFBB", "#FC4E07"))
}
}
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.print(p6)## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
# 5-6-2. After excluding NCSU data
# Before analyzing this, subsetting excluding NCSU data (182 ppl, 48.1%)
#(dim(data[data$source=='NCSU',])[1]/dim(data)[1])*100
data_ex_ncsu <- data[data$source=='NCSU',]
data_renamed <- data_ex_ncsu[, c('log_ast_alt', 'log_ast', 'log_alt', 'case_control')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'Cirrhosis diagnosed')
# 6. A frequency table of categorical outcomes
# 6-1. Total ppl
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
frequency_tables <- lapply(data_renamed, function(x) {
tbl <- as.data.frame(table(x))
colnames(tbl) <- c("Category", "Frequency")
return(tbl)
})
combined_freq_df <- bind_rows(lapply(names(frequency_tables), function(name) {
df <- frequency_tables[[name]]
df$Variable <- name
return(df)
}))
combined_freq_df <- combined_freq_df %>%
pivot_wider(names_from = Variable, values_from = Frequency, values_fill = list(Frequency = 0)) %>%
arrange(Category)
combined_freq_df <- combined_freq_df %>%
dplyr::select(Category, everything())
combined_freq_df$Category <- as.character(combined_freq_df$Category)
total_row <- colSums(combined_freq_df[,-1])
total_row <- c("Total", total_row)
combined_freq_df <- rbind(combined_freq_df, total_row)
fancy_table1 <- flextable(combined_freq_df)
fancy_table1 <- set_table_properties(fancy_table1, width = 0.8, layout = "autofit")
fancy_table1 <- set_caption(fancy_table1, caption = "Frequency tables for categorical endpoints in total ppl (N=349)")
fancy_table1 <- fontsize(fancy_table1, size = 8, part = "body") # Reduce font size of values
fancy_table1 <- fontsize(fancy_table1, size = 9, part = "header") # Reduce font size of header
fancy_table1 <- border(fancy_table1, i = (nrow(fancy_table1$body$dataset)-1), border.bottom = fp_border(color = "black"))
fancy_table1 <- align(fancy_table1, align = "center", part = "body")
print(fancy_table1)## a flextable object.
## col_keys: `Category`, `lowest cut ast`, `highest cut ast`, `lowest cut alt`, `highest cut alt`, `cirrhosis`, `cirrhosis diagnosed`
## header has 1 row(s)
## body has 3 row(s)
## original dataset sample:
## Category lowest cut ast highest cut ast lowest cut alt highest cut alt
## 1 Abnormal 222 165 49 28
## 2 Normal 127 184 300 321
## 3 Total 349 349 349 349
## cirrhosis cirrhosis diagnosed
## 1 341 137
## 2 8 206
## 3 349 343# 6-2. After excluding Total ppl
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
frequency_tables <- lapply(data_renamed, function(x) {
tbl <- as.data.frame(table(x))
colnames(tbl) <- c("Category", "Frequency")
return(tbl)
})
combined_freq_df <- bind_rows(lapply(names(frequency_tables), function(name) {
df <- frequency_tables[[name]]
df$Variable <- name
return(df)
}))
combined_freq_df <- combined_freq_df %>%
pivot_wider(names_from = Variable, values_from = Frequency, values_fill = list(Frequency = 0)) %>%
arrange(Category)
combined_freq_df <- combined_freq_df %>%
dplyr::select(Category, everything())
combined_freq_df$Category <- as.character(combined_freq_df$Category)
total_row <- colSums(combined_freq_df[,-1])
total_row <- c("Total", total_row)
combined_freq_df <- rbind(combined_freq_df, total_row)
fancy_table2 <- flextable(combined_freq_df)
fancy_table2 <- set_table_properties(fancy_table2, width = 0.8, layout = "autofit")
fancy_table2 <- set_caption(fancy_table2, caption = "Frequency tables for categorical endpoints after exlcuding NCSU (N=173)")
fancy_table2 <- fontsize(fancy_table2, size = 8, part = "body") # Reduce font size of values
fancy_table2 <- fontsize(fancy_table2, size = 9, part = "header") # Reduce font size of header
fancy_table2 <- border(fancy_table2, i = (nrow(fancy_table2$body$dataset)-1), border.bottom = fp_border(color = "black"))
fancy_table2 <- align(fancy_table2, align = "center", part = "body")
print(fancy_table2)## a flextable object.
## col_keys: `Category`, `lowest cut ast`, `highest cut ast`, `lowest cut alt`, `highest cut alt`, `cirrhosis`, `cirrhosis diagnosed`
## header has 1 row(s)
## body has 3 row(s)
## original dataset sample:
## Category lowest cut ast highest cut ast lowest cut alt highest cut alt
## 1 Abnormal 88 43 10 5
## 2 Normal 85 130 163 168
## 3 Total 173 173 173 173
## cirrhosis cirrhosis diagnosed
## 1 170 2
## 2 3 145
## 3 173 147# Create a new Word document
doc <- read_docx()
# Add the first flextable
doc <- body_add_flextable(doc, value = fancy_table1)
# Add a page break (optional) or additional spacing
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")
# Add the second flextable
doc <- body_add_flextable(doc, value = fancy_table2)
# Save the document
print(doc, target = "Frequency tables of categorical outcome vars.docx")
# 7. Association between cat endpoints
# 7-1 through correlation matrix
# 7-1-1. total ppl
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
# Relabel the 'cirrhosis' variables
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
# Create dummy variables and compute correlation matrix
correlation_matrix <- model.matrix(~0 + ., data=data_renamed) %>%
cor(use="pairwise.complete.obs")
# Plot the correlation matrix using ggcorrplot
plot6 <-ggcorrplot(correlation_matrix,
show.diag=FALSE,
type="lower",
lab=TRUE,
lab_size=2,
title="Correlation matrix of categorical outcomes in total ppl (N=349)") + # Title of the plot
theme(
plot.title = element_text(size = 10), # Title font size
axis.text.x = element_text(size = 8), # X-axis title font size
axis.text.y = element_text(size = 8) # Y-axis title font size
)
print(plot6)
# 7-1-2. after excluding NCSU data
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
# Relabel the 'cirrhosis' variables
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
levels = c("Healthy", "Cirrhosis"),
labels = c("Normal", "Abnormal"))
# Create dummy variables and compute correlation matrix
correlation_matrix <- model.matrix(~0 + ., data=data_renamed) %>%
cor(use="pairwise.complete.obs")
# Plot the correlation matrix using ggcorrplot
plot6 <-ggcorrplot(correlation_matrix,
show.diag=FALSE,
type="lower",
lab=TRUE,
lab_size=2,
title="Correlation matrix of categorical outcomes after excluding NCSU data (N=173)") + # Title of the plot
theme(
plot.title = element_text(size = 10), # Title font size
axis.text.x = element_text(size = 8), # X-axis title font size
axis.text.y = element_text(size = 8) # Y-axis title font size
)
print(plot6)
###########Tabulate
# 1. total ppl
# Create dummy variables
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
model_matrix <- model.matrix(~0 + ., data = data_renamed)
correlation_matrix <- cor(model_matrix, use = "pairwise.complete.obs")
# Function to calculate p-values for correlations
cor.mtest <- function(mat, ...) {
mat <- as.matrix(mat)
n <- ncol(mat)
p.mat <- matrix(NA, n, n)
diag(p.mat) <- 0
for (i in 1:(n - 1)) {
for (j in (i + 1):n) {
tmp <- cor.test(mat[, i], mat[, j], ...)
p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
}
}
colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
p.mat
}
# Calculate p-values
p.mat <- cor.mtest(model_matrix)
# Convert correlation matrix and p-value matrix to long format
cor_long <- melt(correlation_matrix)
pval_long <- melt(p.mat)
# Rename columns for merging
colnames(cor_long) <- c("Var1", "Var2", "correlation")
colnames(pval_long) <- c("Var1", "Var2", "pvalue")
# Merge correlation and p-value data frames
plot_df <- merge(cor_long, pval_long, by = c("Var1", "Var2"))
# Check the structure and content of the merged data frame
print("Structure of plot_df:")## [1] "Structure of plot_df:"str(plot_df)## 'data.frame': 49 obs. of 4 variables:
## $ Var1 : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 7 7 7 7 7 7 5 5 5 ...
## $ Var2 : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 5 3 4 1 2 6 7 5 3 ...
## $ correlation: num 1 0.0582 0.3336 0.1052 -0.2435 ...
## $ pvalue : num 0 0.30221339661 0.00000000118 0.06181134671 0.000011963 ...print("First few rows of plot_df:")## [1] "First few rows of plot_df:"head(plot_df)
print(significant_results)
## # A tibble: 98 × 6
## pfas term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 pf_hx_s age_at_enrollment 0.0363 0.00857 4.24 2.89e-5
## 2 pf_hx_s smokingUnknown/Not Reported -0.362 0.183 -1.97 4.91e-2
## 3 pf_hx_s sq_water_bottledUnknown/Not Rep… -0.771 0.219 -3.52 4.91e-4
## 4 pf_hx_s sq_water_bottledYes -0.671 0.212 -3.17 1.65e-3
## 5 pfda age_at_enrollment 0.00451 0.00178 2.54 1.15e-2
## 6 pfda smokingSmoke or use vape -0.154 0.0576 -2.67 7.96e-3
## 7 pfda smokingUnknown/Not Reported -0.0864 0.0372 -2.32 2.07e-2
## 8 pfda sq_drink_alcoholYes, current dr… 0.177 0.0469 3.78 1.80e-4
## 9 pfda sq_water_bottledUnknown/Not Rep… -0.109 0.0452 -2.41 1.62e-2
## 10 pfda sq_water_bottledYes -0.105 0.0436 -2.41 1.65e-2
## # ℹ 88 more rows# Create a formatted table using gt
significant_results %>%
gt() %>%
tab_header(
title = "Significant Associations between PFAS and potential confounders From Simple LR"
) %>%
cols_label(
pfas = "PFAS",
term = "Covariate",
estimate = "Estimate",
std.error = "Standard Error",
statistic = "Statistic",
p.value = "P-value"
) %>%
fmt_number(
columns = c(estimate, std.error, statistic, p.value),
decimals = 3
) %>%
tab_source_note(
source_note = "Significant results with p-value < 0.05"
)| Significant Associations between PFAS and potential confounders From Simple LR | |||||
| PFAS | Covariate | Estimate | Standard Error | Statistic | P-value |
|---|---|---|---|---|---|
| pf_hx_s | age_at_enrollment | 0.036 | 0.009 | 4.237 | 0.000 |
| pf_hx_s | smokingUnknown/Not Reported | −0.362 | 0.183 | −1.974 | 0.049 |
| pf_hx_s | sq_water_bottledUnknown/Not Reported | −0.771 | 0.219 | −3.516 | 0.000 |
| pf_hx_s | sq_water_bottledYes | −0.671 | 0.212 | −3.169 | 0.002 |
| pfda | age_at_enrollment | 0.005 | 0.002 | 2.540 | 0.012 |
| pfda | smokingSmoke or use vape | −0.154 | 0.058 | −2.668 | 0.008 |
| pfda | smokingUnknown/Not Reported | −0.086 | 0.037 | −2.324 | 0.021 |
| pfda | sq_drink_alcoholYes, current drinker | 0.177 | 0.047 | 3.784 | 0.000 |
| pfda | sq_water_bottledUnknown/Not Reported | −0.109 | 0.045 | −2.415 | 0.016 |
| pfda | sq_water_bottledYes | −0.105 | 0.044 | −2.408 | 0.017 |
| pfna | age_at_enrollment | 0.012 | 0.003 | 4.197 | 0.000 |
| pfna | smokingSmoke or use vape | −0.294 | 0.096 | −3.079 | 0.002 |
| pfna | smokingUnknown/Not Reported | −0.130 | 0.062 | −2.107 | 0.036 |
| pfna | sq_drink_alcoholYes, current drinker | 0.292 | 0.078 | 3.757 | 0.000 |
| pfna | sq_water_bottledUnknown/Not Reported | −0.169 | 0.075 | −2.253 | 0.025 |
| pfos | sourceUNC | −3.388 | 1.678 | −2.020 | 0.044 |
| pfos | age_at_enrollment | 0.196 | 0.035 | 5.619 | 0.000 |
| pfos | race_final_labelMore than one race | 17.960 | 7.933 | 2.264 | 0.024 |
| pfos | smokingSmoke or use vape | −3.693 | 1.163 | −3.175 | 0.002 |
| pfos | sq_drink_alcoholYes, current drinker | 2.235 | 0.961 | 2.326 | 0.021 |
| pfos | sq_water_bottledUnknown/Not Reported | −2.434 | 0.909 | −2.677 | 0.008 |
| pfos | sq_water_bottledYes | −2.587 | 0.878 | −2.948 | 0.003 |
| pf_hp_a | sourceNCSU | −0.019 | 0.007 | −2.768 | 0.006 |
| pf_hp_a | race_final_labelAmerican Indian/Alaskan Native | −0.198 | 0.044 | −4.557 | 0.000 |
| pf_hp_a | race_final_labelAsian | −0.170 | 0.046 | −3.703 | 0.000 |
| pf_hp_a | race_final_labelAsian/Pacific Islander | −0.169 | 0.041 | −4.124 | 0.000 |
| pf_hp_a | race_final_labelBlack | −0.187 | 0.036 | −5.202 | 0.000 |
| pf_hp_a | race_final_labelMore than one race | −0.151 | 0.062 | −2.451 | 0.015 |
| pf_hp_a | race_final_labelOther | −0.187 | 0.038 | −4.879 | 0.000 |
| pf_hp_a | race_final_labelUnknown/Not Reported | −0.193 | 0.040 | −4.779 | 0.000 |
| pf_hp_a | race_final_labelWhite | −0.159 | 0.036 | −4.442 | 0.000 |
| pfbs | ruralLiving in rural area | 0.042 | 0.019 | 2.172 | 0.030 |
| pfbs | sq_average_drink_per_day3-4 alcoholic drinks per day | 0.150 | 0.049 | 3.076 | 0.002 |
| pfoa | age_at_enrollment | 0.029 | 0.008 | 3.628 | 0.000 |
| pfoa | smokingSmoke or use vape | −0.601 | 0.264 | −2.279 | 0.023 |
| pfoa | sq_drink_alcoholYes, current drinker | 0.764 | 0.214 | 3.573 | 0.000 |
| pfoa | sq_water_bottledUnknown/Not Reported | −0.641 | 0.204 | −3.140 | 0.002 |
| pfoa | sq_water_bottledYes | −0.674 | 0.197 | −3.420 | 0.001 |
| pf_pe_a | sourceEmory | 0.019 | 0.005 | 4.093 | 0.000 |
| pf_pe_a | sourceNCSU | 0.011 | 0.004 | 2.607 | 0.009 |
| pf_pe_a | sourceUNC | 0.030 | 0.008 | 3.662 | 0.000 |
| pf_pe_a | sq_water_house_filtrationYes | 0.016 | 0.006 | 2.742 | 0.006 |
| pf_un_a | sourceNCSU | 0.041 | 0.018 | 2.246 | 0.025 |
| pf_un_a | sourceUNC | −0.084 | 0.036 | −2.309 | 0.021 |
| pf_un_a | age_at_enrollment | 0.002 | 0.001 | 2.654 | 0.008 |
| pf_un_a | race_eth_labelNHO | 0.107 | 0.049 | 2.174 | 0.030 |
| pf_un_a | race_final_labelAsian/Pacific Islander | 0.275 | 0.115 | 2.396 | 0.017 |
| pf_un_a | smokingSmoke or use vape | −0.080 | 0.025 | −3.141 | 0.002 |
| pf_un_a | smokingUnknown/Not Reported | −0.046 | 0.016 | −2.786 | 0.006 |
| pf_un_a | sq_drink_alcoholYes, current drinker | 0.074 | 0.021 | 3.557 | 0.000 |
| pf_un_a | sq_average_drink_per_dayUnknown/Not Reported | −0.085 | 0.034 | −2.489 | 0.013 |
| pf_un_a | sq_self_hep_bUnknown/Not Reported | −0.032 | 0.016 | −2.017 | 0.044 |
| pf_un_a | sq_self_hep_cUnknown/Not Reported | −0.033 | 0.016 | −2.040 | 0.042 |
| pf_un_a | sq_water_bottledUnknown/Not Reported | −0.050 | 0.020 | −2.500 | 0.013 |
| pf_un_a | sq_water_bottledYes | −0.040 | 0.019 | −2.076 | 0.039 |
| pf_hp_s | sourceUNC | −0.089 | 0.044 | −2.017 | 0.044 |
| pf_hp_s | age_at_enrollment | 0.006 | 0.001 | 7.001 | 0.000 |
| pf_hp_s | sexMale | 0.052 | 0.018 | 2.844 | 0.005 |
| pf_hp_s | smokingSmoke or use vape | −0.102 | 0.031 | −3.340 | 0.001 |
| pf_hp_s | sq_water_bottledUnknown/Not Reported | −0.059 | 0.024 | −2.458 | 0.014 |
| pf_hp_s | sq_water_bottledYes | −0.074 | 0.023 | −3.212 | 0.001 |
| pf_do_a | sourceUNC | −0.017 | 0.009 | −1.994 | 0.047 |
| pf_do_a | sq_drink_alcoholYes, current drinker | 0.012 | 0.005 | 2.338 | 0.020 |
| pf_do_a | sq_water_bottledYes | −0.009 | 0.005 | −2.025 | 0.044 |
| pf_pe_s | sourceNCSU | 0.008 | 0.004 | 2.077 | 0.038 |
| pf_pe_s | race_final_labelAmerican Indian/Alaskan Native | −0.062 | 0.026 | −2.375 | 0.018 |
| pf_pe_s | race_final_labelAsian/Pacific Islander | −0.055 | 0.025 | −2.217 | 0.027 |
| pf_pe_s | race_final_labelBlack | −0.051 | 0.022 | −2.355 | 0.019 |
| pf_pe_s | race_final_labelOther | −0.053 | 0.023 | −2.281 | 0.023 |
| pf_pe_s | race_final_labelUnknown/Not Reported | −0.053 | 0.024 | −2.180 | 0.030 |
| pf_pe_s | sq_average_drink_per_dayUnknown/Not Reported | −0.016 | 0.007 | −2.200 | 0.028 |
| pf_pe_s | sq_water_tap_unfilteredYes | 0.008 | 0.004 | 1.996 | 0.047 |
| pf_pe_s | sq_water_bottledUnknown/Not Reported | −0.009 | 0.004 | −2.186 | 0.029 |
| pf_pe_s | sq_water_bottledYes | −0.016 | 0.004 | −3.935 | 0.000 |
| pf_hx_a | sourceEmory | −0.005 | 0.001 | −3.606 | 0.000 |
| pf_hx_a | sourceUNC | 0.009 | 0.003 | 3.406 | 0.001 |
| pf_hx_a | smokingSmoke or use vape | 0.004 | 0.002 | 2.158 | 0.032 |
| pf_hx_a | smokingUnknown/Not Reported | 0.004 | 0.001 | 3.698 | 0.000 |
| pf_hx_a | sq_drink_alcoholUnknown/Not Reported | 0.004 | 0.002 | 2.738 | 0.006 |
| pf_hx_a | sq_self_hep_bUnknown/Not Reported | 0.003 | 0.001 | 3.020 | 0.003 |
| pf_hx_a | sq_self_hep_cUnknown/Not Reported | 0.004 | 0.001 | 3.441 | 0.001 |
| pf_hx_a | sq_water_wellUnknown/Not Reported | 0.003 | 0.001 | 2.558 | 0.011 |
| pf_hx_a | sq_water_tap_unfilteredUnknown/Not Reported | 0.004 | 0.001 | 2.680 | 0.008 |
| pf_hx_a | sq_water_house_filtrationUnknown/Not Reported | 0.003 | 0.001 | 2.555 | 0.011 |
| pf_hx_a | sq_water_faucet_filterUnknown/Not Reported | 0.003 | 0.001 | 2.757 | 0.006 |
| pf_hx_a | sq_water_charcoal_filterUnknown/Not Reported | 0.003 | 0.001 | 2.622 | 0.009 |
| pf_hx_a | sq_water_bottledUnknown/Not Reported | 0.003 | 0.001 | 2.349 | 0.019 |
| pf_hx_a | sq_water_noneUnknown/Not Reported | 0.003 | 0.001 | 2.639 | 0.009 |
| pf_hx_a | sq_water_other_typeUnknown/Not Reported | 0.003 | 0.001 | 3.163 | 0.002 |
| pf_hx_a | sq_water_other_typeYes | 0.006 | 0.002 | 2.808 | 0.005 |
| pfba | sourceEmory | 0.032 | 0.009 | 3.516 | 0.000 |
| pfba | sexMale | 0.020 | 0.007 | 2.994 | 0.003 |
| pfba | race_final_labelBlack | −0.092 | 0.045 | −2.037 | 0.042 |
| pfba | race_final_labelOther | −0.099 | 0.048 | −2.053 | 0.041 |
| pfba | sq_drink_alcoholNo, never drinker | −0.021 | 0.010 | −2.105 | 0.036 |
| pfba | sq_drink_alcoholUnknown/Not Reported | −0.019 | 0.009 | −2.050 | 0.041 |
| pfba | sq_drink_alcoholYes, current drinker | −0.025 | 0.009 | −2.677 | 0.008 |
| pfba | sq_water_faucet_filterYes | 0.027 | 0.008 | 3.336 | 0.001 |
| Significant results with p-value < 0.05 | |||||
# Remove duplicate comparisons (keep only one direction)
plot_df <- plot_df %>%
mutate(Var1 = as.character(Var1), Var2 = as.character(Var2)) %>%
arrange(Var1, Var2) %>%
distinct(Var1, Var2, .keep_all = TRUE) %>%
filter(Var1 < Var2) # Keep only one direction of the comparison
# Ensure pvalue is numeric for formatting
plot_df <- plot_df %>%
mutate(pvalue = as.numeric(pvalue)) %>%
mutate(pvalue = ifelse(pvalue < 0.001, "<0.001", sprintf("%.3f", pvalue)),
correlation = sprintf("%.3f", correlation))
# Check the updated plot_df
print("Structure of updated plot_df:")## [1] "Structure of updated plot_df:"str(plot_df)## 'data.frame': 21 obs. of 4 variables:
## $ Var1 : chr "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" ...
## $ Var2 : chr "`highest cut alt`Normal" "`highest cut ast`Normal" "`lowest cut alt`Normal" "`lowest cut ast`Abnormal" ...
## $ correlation: chr "0.058" "0.334" "0.105" "-0.244" ...
## $ pvalue : chr "0.302" "<0.001" "0.062" "<0.001" ...print("First few rows of updated plot_df:")## [1] "First few rows of updated plot_df:"head(plot_df)ft <- flextable(plot_df)
ft <- set_table_properties(ft, width = 0.75, layout = "autofit") %>%
set_header_labels(values = c(Var1 = "Cat Endpoint 1", Var2 = "Cat Endpoint 2", correlation = "Correlation", pvalue = "P")) %>%
color(color = "#000000") %>%
fontsize(size = 9)
# Add caption
ft <- set_caption(ft, caption = "Inter-correlation table of categorical endpoints in total ppl (N=349)")
print(ft) ## a flextable object.
## col_keys: `Var1`, `Var2`, `correlation`, `pvalue`
## header has 1 row(s)
## body has 21 row(s)
## original dataset sample:
## Var1 Var2 correlation pvalue
## 1 `cirrhosis diagnosed`Healthy `highest cut alt`Normal 0.058 0.302
## 2 `cirrhosis diagnosed`Healthy `highest cut ast`Normal 0.334 <0.001
## 3 `cirrhosis diagnosed`Healthy `lowest cut alt`Normal 0.105 0.062
## 4 `cirrhosis diagnosed`Healthy `lowest cut ast`Abnormal -0.244 <0.001
## 5 `cirrhosis diagnosed`Healthy `lowest cut ast`Normal 0.244 <0.001# 2. after excluding NCSU data
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')
model_matrix <- model.matrix(~0 + ., data = data_renamed)
correlation_matrix <- cor(model_matrix, use = "pairwise.complete.obs")
cor.mtest <- function(mat, ...) {
mat <- as.matrix(mat)
n <- ncol(mat)
p.mat <- matrix(NA, n, n)
diag(p.mat) <- 0
for (i in 1:(n - 1)) {
for (j in (i + 1):n) {
tmp <- cor.test(mat[, i], mat[, j], ...)
p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
}
}
colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
p.mat
}
p.mat <- cor.mtest(model_matrix)
cor_long <- melt(correlation_matrix)
pval_long <- melt(p.mat)
colnames(cor_long) <- c("Var1", "Var2", "correlation")
colnames(pval_long) <- c("Var1", "Var2", "pvalue")
plot_df <- merge(cor_long, pval_long, by = c("Var1", "Var2"))
print("Structure of plot_df:")## [1] "Structure of plot_df:"str(plot_df)## 'data.frame': 49 obs. of 4 variables:
## $ Var1 : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 7 7 7 7 7 7 5 5 5 ...
## $ Var2 : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 5 3 4 1 2 6 7 5 3 ...
## $ correlation: num 1 -0.0232 0.0695 -0.0296 -0.1221 ...
## $ pvalue : num 0 0.786 0.414 0.728 0.151 ...print("First few rows of plot_df:")## [1] "First few rows of plot_df:"head(plot_df)plot_df <- plot_df %>%
mutate(Var1 = as.character(Var1), Var2 = as.character(Var2)) %>%
arrange(Var1, Var2) %>%
distinct(Var1, Var2, .keep_all = TRUE) %>%
filter(Var1 < Var2) # Keep only one direction of the comparison
# Ensure pvalue is numeric for formatting
plot_df <- plot_df %>%
mutate(pvalue = as.numeric(pvalue)) %>%
mutate(pvalue = ifelse(pvalue < 0.001, "<0.001", sprintf("%.3f", pvalue)),
correlation = sprintf("%.3f", correlation))
print("Structure of updated plot_df:")## [1] "Structure of updated plot_df:"str(plot_df)## 'data.frame': 21 obs. of 4 variables:
## $ Var1 : chr "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" ...
## $ Var2 : chr "`highest cut alt`Normal" "`highest cut ast`Normal" "`lowest cut alt`Normal" "`lowest cut ast`Abnormal" ...
## $ correlation: chr "-0.023" "0.070" "-0.030" "-0.122" ...
## $ pvalue : chr "0.786" "0.414" "0.728" "0.151" ...print("First few rows of updated plot_df:")## [1] "First few rows of updated plot_df:"head(plot_df)ft1 <- flextable(plot_df)
ft1 <- set_table_properties(ft1, width = 0.75, layout = "autofit") %>%
set_header_labels(values = c(Var1 = "Cat Endpoint 1", Var2 = "Cat Endpoint 2", correlation = "Correlation", pvalue = "P")) %>%
color(color = "#000000") %>%
fontsize(size = 9)
# Add caption
ft1 <- set_caption(ft1, caption = "Inter-correlation table of categorical endpoints after excluding NCSU data (N=173)")
print(ft1) ## a flextable object.
## col_keys: `Var1`, `Var2`, `correlation`, `pvalue`
## header has 1 row(s)
## body has 21 row(s)
## original dataset sample:
## Var1 Var2 correlation pvalue
## 1 `cirrhosis diagnosed`Healthy `highest cut alt`Normal -0.023 0.786
## 2 `cirrhosis diagnosed`Healthy `highest cut ast`Normal 0.070 0.414
## 3 `cirrhosis diagnosed`Healthy `lowest cut alt`Normal -0.030 0.728
## 4 `cirrhosis diagnosed`Healthy `lowest cut ast`Abnormal -0.122 0.151
## 5 `cirrhosis diagnosed`Healthy `lowest cut ast`Normal 0.122 0.151#save
doc <- read_docx()
doc <- body_add_flextable(doc, ft)
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_flextable(doc, ft1)
print(doc, target = "Correlation of intercategorical endpoints.docx")Diagnosis of categorical endpoints
#1. endpoint diagnosis
results <- list()
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1', 'alt_cat2', 'cirrhosis', 'case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
variables <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')
for (i in seq_along(variables)) {
for (j in seq_along(variables)) {
if (i != j) {
# Ensure both variables have the same length and no NAs
if (length(data_renamed[[variables[i]]]) == length(data_renamed[[variables[j]]])) {
# Remove rows with NA values in either variable
valid_indices <- complete.cases(data_renamed[[variables[i]]], data_renamed[[variables[j]]])
valid_data_i <- data_renamed[[variables[i]]][valid_indices]
valid_data_j <- data_renamed[[variables[j]]][valid_indices]
# Print data for debugging
print(paste("Data for", variables[i], "vs", variables[j]))
print(head(valid_data_i))
print(head(valid_data_j))
# Generate confusion matrix
conf_matrix <- table(predicted = valid_data_i, actual = valid_data_j)
# Print confusion matrix for debugging
print(paste("Confusion Matrix for", variables[i], "vs", variables[j]))
print(conf_matrix)
# Get levels from confusion matrix
levels_i <- rownames(conf_matrix)
levels_j <- colnames(conf_matrix)
# Initialize metric values
sensitivity <- specificity <- ppv <- npv <- accuracy <- auc_value <- f1_score <- kappa_value <- NA
# Check if both 'Abnormal/cirrhosis' and 'Normal' are present in confusion matrix
if ("Abnormal" %in% levels_i && "Abnormal" %in% levels_j &&
"Normal" %in% levels_i && "Normal" %in% levels_j) {
# Extract values safely
true_pos <- conf_matrix["Abnormal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
false_neg <- conf_matrix["Abnormal", "Normal"] %>% ifelse(is.na(.), 0, .)
false_pos <- conf_matrix["Normal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
true_neg <- conf_matrix["Normal", "Normal"] %>% ifelse(is.na(.), 0, .)
# Calculate metrics
sensitivity <- true_pos / (true_pos + false_neg)
specificity <- true_neg / (true_neg + false_pos)
ppv <- true_pos / (true_pos + false_pos)
npv <- true_neg / (true_neg + false_neg)
accuracy <- (true_pos + true_neg) / sum(conf_matrix)
# Ensure valid_data_i is numeric and valid_data_j is a factor
valid_data_i_numeric <- as.numeric(factor(valid_data_i, levels = unique(valid_data_i)))
valid_data_j_factor <- factor(valid_data_j, levels = c("Normal", "Abnormal")) # Assuming "High" is positive class
# Print data for ROC debugging
print("Valid Data for ROC")
print(head(valid_data_i_numeric))
print(head(valid_data_j_factor))
# ROC Curve and AUC
roc_curve <- tryCatch({
roc(valid_data_j_factor, valid_data_i_numeric)
}, error = function(e) {
print("Error in ROC calculation")
return(NULL)
})
auc_value <- if (!is.null(roc_curve)) auc(roc_curve) else NA
# F1 Score
f1_score <- if (!is.na(ppv + sensitivity) && ppv + sensitivity > 0) {
2 * (ppv * sensitivity) / (ppv + sensitivity)
} else {
NA
}
# Kappa Statistic
kappa_value <- tryCatch({
kappa2(conf_matrix)$value
}, error = function(e) {
print("Error in Kappa calculation")
return(NA)
})
}
# Store results
results[[paste(variables[i], "vs", variables[j])]] <- c(
Sensitivity = sensitivity,
Specificity = specificity,
PPV = ppv,
NPV = npv,
Accuracy = accuracy,
AUC = auc_value,
F1_Score = f1_score,
Kappa = kappa_value
)
} else {
results[[paste(variables[i], "vs", variables[j])]] <- c(
Sensitivity = NA,
Specificity = NA,
PPV = NA,
NPV = NA,
Accuracy = NA,
AUC = NA,
F1_Score = NA,
Kappa = NA
)
}
}
}
}## [1] "Data for lowest cut ast vs highest cut ast"
## [1] "Normal" "Abnormal" "Normal" "Abnormal" "Normal" "Abnormal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Abnormal"
## [1] "Confusion Matrix for lowest cut ast vs highest cut ast"
## actual
## predicted Abnormal Normal
## Abnormal 165 57
## Normal 0 127
## [1] "Valid Data for ROC"
## [1] 1 2 1 2 1 2
## [1] Normal Normal Normal Normal Normal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal## Setting direction: controls < cases## [1] "Data for lowest cut ast vs lowest cut alt"
## [1] "Normal" "Abnormal" "Normal" "Abnormal" "Normal" "Abnormal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for lowest cut ast vs lowest cut alt"
## actual
## predicted Abnormal Normal
## Abnormal 49 173
## Normal 0 127
## [1] "Valid Data for ROC"
## [1] 1 2 1 2 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for lowest cut ast vs highest cut alt"
## [1] "Normal" "Abnormal" "Normal" "Abnormal" "Normal" "Abnormal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for lowest cut ast vs highest cut alt"
## actual
## predicted Abnormal Normal
## Abnormal 28 194
## Normal 0 127
## [1] "Valid Data for ROC"
## [1] 1 2 1 2 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for lowest cut ast vs cirrhosis"
## [1] "Normal" "Abnormal" "Normal" "Abnormal" "Normal" "Abnormal"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for lowest cut ast vs cirrhosis"
## actual
## predicted Abnormal Normal
## Abnormal 215 7
## Normal 126 1
## [1] "Valid Data for ROC"
## [1] 1 2 1 2 1 2
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for lowest cut ast vs cirrhosis diagnosed"
## [1] "Normal" "Abnormal" "Normal" "Abnormal" "Normal" "Abnormal"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for lowest cut ast vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 101 102
## Normal 28 85
## [1] "Valid Data for ROC"
## [1] 1 2 1 2 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for highest cut ast vs lowest cut ast"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Abnormal"
## [1] "Normal" "Abnormal" "Normal" "Abnormal" "Normal" "Abnormal"
## [1] "Confusion Matrix for highest cut ast vs lowest cut ast"
## actual
## predicted Abnormal Normal
## Abnormal 165 0
## Normal 57 127
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 2
## [1] Normal Abnormal Normal Abnormal Normal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for highest cut ast vs lowest cut alt"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Abnormal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for highest cut ast vs lowest cut alt"
## actual
## predicted Abnormal Normal
## Abnormal 49 116
## Normal 0 184
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for highest cut ast vs highest cut alt"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Abnormal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for highest cut ast vs highest cut alt"
## actual
## predicted Abnormal Normal
## Abnormal 28 137
## Normal 0 184
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for highest cut ast vs cirrhosis"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Abnormal"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for highest cut ast vs cirrhosis"
## actual
## predicted Abnormal Normal
## Abnormal 158 7
## Normal 183 1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 2
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal## Setting direction: controls > cases## [1] "Data for highest cut ast vs cirrhosis diagnosed"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Abnormal"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for highest cut ast vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 90 67
## Normal 39 120
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal## Setting direction: controls < cases## [1] "Data for lowest cut alt vs lowest cut ast"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal" "Abnormal" "Normal" "Abnormal" "Normal" "Abnormal"
## [1] "Confusion Matrix for lowest cut alt vs lowest cut ast"
## actual
## predicted Abnormal Normal
## Abnormal 49 0
## Normal 173 127
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Abnormal Normal Abnormal Normal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for lowest cut alt vs highest cut ast"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Abnormal"
## [1] "Confusion Matrix for lowest cut alt vs highest cut ast"
## actual
## predicted Abnormal Normal
## Abnormal 49 0
## Normal 116 184
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for lowest cut alt vs highest cut alt"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for lowest cut alt vs highest cut alt"
## actual
## predicted Abnormal Normal
## Abnormal 28 21
## Normal 0 300
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for lowest cut alt vs cirrhosis"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for lowest cut alt vs cirrhosis"
## actual
## predicted Abnormal Normal
## Abnormal 42 7
## Normal 299 1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal## Setting direction: controls > cases## [1] "Data for lowest cut alt vs cirrhosis diagnosed"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for lowest cut alt vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 25 22
## Normal 104 165
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal## Setting direction: controls < cases## [1] "Data for highest cut alt vs lowest cut ast"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal" "Abnormal" "Normal" "Abnormal" "Normal" "Abnormal"
## [1] "Confusion Matrix for highest cut alt vs lowest cut ast"
## actual
## predicted Abnormal Normal
## Abnormal 28 0
## Normal 194 127
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Abnormal Normal Abnormal Normal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for highest cut alt vs highest cut ast"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Abnormal"
## [1] "Confusion Matrix for highest cut alt vs highest cut ast"
## actual
## predicted Abnormal Normal
## Abnormal 28 0
## Normal 137 184
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for highest cut alt vs lowest cut alt"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for highest cut alt vs lowest cut alt"
## actual
## predicted Abnormal Normal
## Abnormal 28 0
## Normal 21 300
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for highest cut alt vs cirrhosis"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for highest cut alt vs cirrhosis"
## actual
## predicted Abnormal Normal
## Abnormal 21 7
## Normal 320 1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal## Setting direction: controls > cases## [1] "Data for highest cut alt vs cirrhosis diagnosed"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for highest cut alt vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 14 14
## Normal 115 173
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal## Setting direction: controls < cases## [1] "Data for cirrhosis vs lowest cut ast"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Normal" "Abnormal" "Normal" "Abnormal" "Normal" "Abnormal"
## [1] "Confusion Matrix for cirrhosis vs lowest cut ast"
## actual
## predicted Abnormal Normal
## Abnormal 215 126
## Normal 7 1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Abnormal Normal Abnormal Normal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for cirrhosis vs highest cut ast"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Abnormal"
## [1] "Confusion Matrix for cirrhosis vs highest cut ast"
## actual
## predicted Abnormal Normal
## Abnormal 158 183
## Normal 7 1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for cirrhosis vs lowest cut alt"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for cirrhosis vs lowest cut alt"
## actual
## predicted Abnormal Normal
## Abnormal 42 299
## Normal 7 1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for cirrhosis vs highest cut alt"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for cirrhosis vs highest cut alt"
## actual
## predicted Abnormal Normal
## Abnormal 21 320
## Normal 7 1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for cirrhosis vs cirrhosis diagnosed"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for cirrhosis vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 128 180
## Normal 1 7
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for cirrhosis diagnosed vs lowest cut ast"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Normal" "Abnormal" "Normal" "Abnormal" "Normal" "Abnormal"
## [1] "Confusion Matrix for cirrhosis diagnosed vs lowest cut ast"
## actual
## predicted Abnormal Normal
## Abnormal 101 28
## Normal 102 85
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Abnormal Normal Abnormal Normal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for cirrhosis diagnosed vs highest cut ast"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Abnormal"
## [1] "Confusion Matrix for cirrhosis diagnosed vs highest cut ast"
## actual
## predicted Abnormal Normal
## Abnormal 90 39
## Normal 67 120
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for cirrhosis diagnosed vs lowest cut alt"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for cirrhosis diagnosed vs lowest cut alt"
## actual
## predicted Abnormal Normal
## Abnormal 25 104
## Normal 22 165
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for cirrhosis diagnosed vs highest cut alt"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for cirrhosis diagnosed vs highest cut alt"
## actual
## predicted Abnormal Normal
## Abnormal 14 115
## Normal 14 173
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Data for cirrhosis diagnosed vs cirrhosis"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for cirrhosis diagnosed vs cirrhosis"
## actual
## predicted Abnormal Normal
## Abnormal 128 1
## Normal 180 7
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Normal Abnormal## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < casesresults_df <- do.call(rbind, results)
results_df <- as.data.frame(results_df)
results_df <- results_df %>% dplyr::select(-AUC)
results_df <- results_df %>%
mutate(across(everything(), ~ case_when(
. == 1.000 ~ "1",
. == 0.000 ~ "0",
TRUE ~ sprintf("%.3f", .)
)))
results_df$Comparison <- rownames(results_df)
results_df <- results_df %>% dplyr::select(Comparison, everything())
fancy_table <- flextable(results_df)
fancy_table <- set_table_properties(fancy_table, width = 0.8, layout = "autofit")
fancy_table <- set_caption(fancy_table, caption = "Confusion matrix results table (N=349)")
fancy_table <- fontsize(fancy_table, size = 8, part = "all") # Reduce font size of values
fancy_table <- fontsize(fancy_table, size = 9, part = "header") # Reduce font size of header
print(fancy_table)## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa`
## header has 1 row(s)
## body has 30 row(s)
## original dataset sample:
## Comparison
## lowest cut ast vs highest cut ast lowest cut ast vs highest cut ast
## lowest cut ast vs lowest cut alt lowest cut ast vs lowest cut alt
## lowest cut ast vs highest cut alt lowest cut ast vs highest cut alt
## lowest cut ast vs cirrhosis lowest cut ast vs cirrhosis
## lowest cut ast vs cirrhosis diagnosed lowest cut ast vs cirrhosis diagnosed
## Sensitivity Specificity PPV NPV
## lowest cut ast vs highest cut ast 0.743 1 1 0.690
## lowest cut ast vs lowest cut alt 0.221 1 1 0.423
## lowest cut ast vs highest cut alt 0.126 1 1 0.396
## lowest cut ast vs cirrhosis 0.968 0.008 0.630 0.125
## lowest cut ast vs cirrhosis diagnosed 0.498 0.752 0.783 0.455
## Accuracy F1_Score Kappa
## lowest cut ast vs highest cut ast 0.837 0.853 0
## lowest cut ast vs lowest cut alt 0.504 0.362 0
## lowest cut ast vs highest cut alt 0.444 0.224 0
## lowest cut ast vs cirrhosis 0.619 0.764 0
## lowest cut ast vs cirrhosis diagnosed 0.589 0.608 0#2. setting case_control as gold standard in total ppl
results <- list()
seen_comparisons <- character() # Initialize seen_comparisons as a character vector
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1', 'alt_cat2', 'cirrhosis', 'case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
variables <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')
# Identify the gold standard variable
gold_standard <- 'cirrhosis diagnosed'
for (i in seq_along(variables)) {
for (j in seq_along(variables)) {
if (i != j) {
# Define the comparison names
comparison_name_1 <- paste(variables[i], "vs", variables[j])
comparison_name_2 <- paste(variables[j], "vs", variables[i])
# Skip if the reverse comparison has already been processed
if (comparison_name_2 %in% seen_comparisons) {
next
}
# Mark the first comparison as seen
seen_comparisons <- c(seen_comparisons, comparison_name_1)
# Ensure both variables have the same length and no NAs
if (length(data_renamed[[variables[i]]]) == length(data_renamed[[variables[j]]])) {
# Remove rows with NA values in either variable
valid_indices <- complete.cases(data_renamed[[variables[i]]], data_renamed[[variables[j]]])
valid_data_i <- data_renamed[[variables[i]]][valid_indices]
valid_data_j <- data_renamed[[variables[j]]][valid_indices]
# Check if gold standard is involved
if (variables[j] == gold_standard) {
predicted <- valid_data_i
actual <- valid_data_j
} else if (variables[i] == gold_standard) {
predicted <- valid_data_j
actual <- valid_data_i
} else {
next
}
# Generate confusion matrix
conf_matrix <- table(predicted = predicted, actual = actual)
# Print confusion matrix for debugging
print(paste("Confusion Matrix for", comparison_name_1))
print(conf_matrix)
# Check for at least one Abnormal and one Normal class in both predicted and actual
levels_i <- rownames(conf_matrix)
levels_j <- colnames(conf_matrix)
if ("Abnormal" %in% levels_i && "Abnormal" %in% levels_j &&
"Normal" %in% levels_i && "Normal" %in% levels_j) {
# Extract values safely
true_pos <- conf_matrix["Abnormal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
false_neg <- conf_matrix["Abnormal", "Normal"] %>% ifelse(is.na(.), 0, .)
false_pos <- conf_matrix["Normal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
true_neg <- conf_matrix["Normal", "Normal"] %>% ifelse(is.na(.), 0, .)
# Calculate metrics
sensitivity <- true_pos / (true_pos + false_neg)
specificity <- true_neg / (true_neg + false_pos)
ppv <- true_pos / (true_pos + false_pos)
npv <- true_neg / (true_neg + false_neg)
accuracy <- (true_pos + true_neg) / sum(conf_matrix)
# Ensure predicted is numeric and actual is a factor
predicted_numeric <- as.numeric(factor(predicted, levels = unique(predicted)))
actual_factor <- factor(actual, levels = c("Normal", "Abnormal")) # Assuming "Abnormal" is positive class
# ROC Curve and AUC
roc_curve <- tryCatch({
roc(actual_factor, predicted_numeric)
}, error = function(e) {
print("Error in ROC calculation")
return(NULL)
})
auc_value <- if (!is.null(roc_curve)) auc(roc_curve) else NA
# F1 Score
f1_score <- if (!is.na(ppv + sensitivity) && ppv + sensitivity > 0) {
2 * (ppv * sensitivity) / (ppv + sensitivity)
} else {
NA
}
# Kappa Statistic
kappa_value <- tryCatch({
kappa2(conf_matrix)$value
}, error = function(e) {
print("Error in Kappa calculation")
return(NA)
})
# Store results
results[[comparison_name_1]] <- c(
Sensitivity = sensitivity,
Specificity = specificity,
PPV = ppv,
NPV = npv,
Accuracy = accuracy,
AUC = auc_value,
F1_Score = f1_score,
Kappa = kappa_value
)
} else {
results[[comparison_name_1]] <- c(
Sensitivity = NA,
Specificity = NA,
PPV = NA,
NPV = NA,
Accuracy = NA,
AUC = NA,
F1_Score = NA,
Kappa = NA
)
}
} else {
results[[comparison_name_1]] <- c(
Sensitivity = NA,
Specificity = NA,
PPV = NA,
NPV = NA,
Accuracy = NA,
AUC = NA,
F1_Score = NA,
Kappa = NA
)
}
}
}
}## [1] "Confusion Matrix for lowest cut ast vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 101 102
## Normal 28 85## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for highest cut ast vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 90 67
## Normal 39 120## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for lowest cut alt vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 25 22
## Normal 104 165## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for highest cut alt vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 14 14
## Normal 115 173## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for cirrhosis vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 128 180
## Normal 1 7## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases# Convert results to dataframe
results_df <- do.call(rbind, results)
results_df <- as.data.frame(results_df)
# Remove AUC column
results_df <- results_df %>% dplyr::select(-AUC)
# Format results
results_df <- results_df %>%
mutate(across(everything(), ~ case_when(
. == 1.000 ~ "1",
. == 0.000 ~ "0",
TRUE ~ sprintf("%.3f", .)
)))
results_df$Comparison <- rownames(results_df)
# Reorder columns
results_df <- results_df %>% dplyr::select(Comparison, everything())
# Filter to keep only the first comparison result (ignoring reversed comparisons)
results_df <- results_df %>%
filter(!grepl("vs", Comparison) | !duplicated(gsub("vs.*", "", Comparison)))
# Create flextable
fancy_table1 <- flextable(results_df)
fancy_table1 <- set_table_properties(fancy_table1, width = 0.8, layout = "autofit")
fancy_table1 <- set_caption(fancy_table1, caption = "Diagnosis of new categorical endpoints compared to case control status (gold standard) in total ppl (N=349)")
fancy_table1 <- fontsize(fancy_table1, size = 8, part = "all") # Reduce font size of values
fancy_table1 <- fontsize(fancy_table1, size = 9, part = "header") # Reduce font size of header
# Print flextable
print(fancy_table1)## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa`
## header has 1 row(s)
## body has 5 row(s)
## original dataset sample:
## Comparison
## lowest cut ast vs cirrhosis diagnosed lowest cut ast vs cirrhosis diagnosed
## highest cut ast vs cirrhosis diagnosed highest cut ast vs cirrhosis diagnosed
## lowest cut alt vs cirrhosis diagnosed lowest cut alt vs cirrhosis diagnosed
## highest cut alt vs cirrhosis diagnosed highest cut alt vs cirrhosis diagnosed
## cirrhosis vs cirrhosis diagnosed cirrhosis vs cirrhosis diagnosed
## Sensitivity Specificity PPV NPV
## lowest cut ast vs cirrhosis diagnosed 0.498 0.752 0.783 0.455
## highest cut ast vs cirrhosis diagnosed 0.573 0.755 0.698 0.642
## lowest cut alt vs cirrhosis diagnosed 0.532 0.613 0.194 0.882
## highest cut alt vs cirrhosis diagnosed 0.500 0.601 0.109 0.925
## cirrhosis vs cirrhosis diagnosed 0.416 0.875 0.992 0.037
## Accuracy F1_Score Kappa
## lowest cut ast vs cirrhosis diagnosed 0.589 0.608 0
## highest cut ast vs cirrhosis diagnosed 0.665 0.629 0
## lowest cut alt vs cirrhosis diagnosed 0.601 0.284 0
## highest cut alt vs cirrhosis diagnosed 0.592 0.178 0.333
## cirrhosis vs cirrhosis diagnosed 0.427 0.586 0############### scatter plot
results_df <- results_df %>% dplyr::select(-Kappa)
results_long <- results_df %>%
pivot_longer(cols = -Comparison, names_to = "Metric", values_to = "Value")
results_long <- results_long %>%
mutate(Value = as.numeric(Value)) %>%
filter(!is.na(Value))
new_labels <- c(
"cirrhosis vs cirrhosis diagnosed" = "cirrhosis by ast/alt>1",
"highest cut alt vs cirrhosis diagnosed" = "highest cut alt",
"lowest cut alt vs cirrhosis diagnosed" = "lowest cut alt",
"highest cut ast vs cirrhosis diagnosed" = "highest cut ast",
"lowest cut ast vs cirrhosis diagnosed" = "lowest cut ast"
)
palette_colors <- brewer.pal(n = length(unique(results_long$Comparison)), name = "Set1")
# Recode the Comparison column
results_long <- results_long %>%
mutate(Comparison = recode(Comparison, !!!new_labels))
# Create the scatter plot
ggplot(results_long, aes(x = Value, y = Metric, color = Comparison, shape = Comparison)) +
geom_point(size = 3) +
labs(title = "Diagnostic testing in total",
x = "Scores",
y = "Measure of diagnostic test performance",
color = "Comparison",
shape = "Comparison") +
scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1)) + # Adjust x-axis
theme(axis.text.y = element_text(size = 10),
axis.text.x = element_text(size = 10),
axis.title = element_text(size = 12),
legend.position = "right") +
scale_shape_manual(values = 1:length(unique(results_long$Comparison))) +
scale_color_manual(values = palette_colors)
# 3. after excluding NCSU data
results <- list()
seen_comparisons <- character() # Initialize seen_comparisons as a character vector
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1', 'alt_cat2', 'cirrhosis', 'case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
variables <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')
# Identify the gold standard variable
gold_standard <- 'cirrhosis diagnosed'
for (i in seq_along(variables)) {
for (j in seq_along(variables)) {
if (i != j) {
# Define the comparison names
comparison_name_1 <- paste(variables[i], "vs", variables[j])
comparison_name_2 <- paste(variables[j], "vs", variables[i])
# Skip if the reverse comparison has already been processed
if (comparison_name_2 %in% seen_comparisons) {
next
}
# Mark the first comparison as seen
seen_comparisons <- c(seen_comparisons, comparison_name_1)
# Ensure both variables have the same length and no NAs
if (length(data_renamed[[variables[i]]]) == length(data_renamed[[variables[j]]])) {
# Remove rows with NA values in either variable
valid_indices <- complete.cases(data_renamed[[variables[i]]], data_renamed[[variables[j]]])
valid_data_i <- data_renamed[[variables[i]]][valid_indices]
valid_data_j <- data_renamed[[variables[j]]][valid_indices]
# Check if gold standard is involved
if (variables[j] == gold_standard) {
predicted <- valid_data_i
actual <- valid_data_j
} else if (variables[i] == gold_standard) {
predicted <- valid_data_j
actual <- valid_data_i
} else {
next
}
# Generate confusion matrix
conf_matrix <- table(predicted = predicted, actual = actual)
# Print confusion matrix for debugging
print(paste("Confusion Matrix for", comparison_name_1))
print(conf_matrix)
# Check for at least one Abnormal and one Normal class in both predicted and actual
levels_i <- rownames(conf_matrix)
levels_j <- colnames(conf_matrix)
if ("Abnormal" %in% levels_i && "Abnormal" %in% levels_j &&
"Normal" %in% levels_i && "Normal" %in% levels_j) {
# Extract values safely
true_pos <- conf_matrix["Abnormal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
false_neg <- conf_matrix["Abnormal", "Normal"] %>% ifelse(is.na(.), 0, .)
false_pos <- conf_matrix["Normal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
true_neg <- conf_matrix["Normal", "Normal"] %>% ifelse(is.na(.), 0, .)
# Calculate metrics
sensitivity <- true_pos / (true_pos + false_neg)
specificity <- true_neg / (true_neg + false_pos)
ppv <- true_pos / (true_pos + false_pos)
npv <- true_neg / (true_neg + false_neg)
accuracy <- (true_pos + true_neg) / sum(conf_matrix)
# Ensure predicted is numeric and actual is a factor
predicted_numeric <- as.numeric(factor(predicted, levels = unique(predicted)))
actual_factor <- factor(actual, levels = c("Normal", "Abnormal")) # Assuming "Abnormal" is positive class
# ROC Curve and AUC
roc_curve <- tryCatch({
roc(actual_factor, predicted_numeric)
}, error = function(e) {
print("Error in ROC calculation")
return(NULL)
})
auc_value <- if (!is.null(roc_curve)) auc(roc_curve) else NA
# F1 Score
f1_score <- if (!is.na(ppv + sensitivity) && ppv + sensitivity > 0) {
2 * (ppv * sensitivity) / (ppv + sensitivity)
} else {
NA
}
# Kappa Statistic
kappa_value <- tryCatch({
kappa2(conf_matrix)$value
}, error = function(e) {
print("Error in Kappa calculation")
return(NA)
})
# Store results
results[[comparison_name_1]] <- c(
Sensitivity = sensitivity,
Specificity = specificity,
PPV = ppv,
NPV = npv,
Accuracy = accuracy,
AUC = auc_value,
F1_Score = f1_score,
Kappa = kappa_value
)
} else {
results[[comparison_name_1]] <- c(
Sensitivity = NA,
Specificity = NA,
PPV = NA,
NPV = NA,
Accuracy = NA,
AUC = NA,
F1_Score = NA,
Kappa = NA
)
}
} else {
results[[comparison_name_1]] <- c(
Sensitivity = NA,
Specificity = NA,
PPV = NA,
NPV = NA,
Accuracy = NA,
AUC = NA,
F1_Score = NA,
Kappa = NA
)
}
}
}
}## [1] "Confusion Matrix for lowest cut ast vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 2 67
## Normal 0 71## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for highest cut ast vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 1 34
## Normal 1 104## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for lowest cut alt vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 0 8
## Normal 2 130## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for highest cut alt vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 0 5
## Normal 2 133## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for cirrhosis vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 2 135
## Normal 0 3## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases# Convert results to dataframe
results_df <- do.call(rbind, results)
results_df <- as.data.frame(results_df)
# Remove AUC column
results_df <- results_df %>% dplyr::select(-AUC)
# Format results
results_df <- results_df %>%
mutate(across(everything(), ~ case_when(
. == 1.000 ~ "1",
. == 0.000 ~ "0",
TRUE ~ sprintf("%.3f", .)
)))
results_df$Comparison <- rownames(results_df)
# Reorder columns
results_df <- results_df %>% dplyr::select(Comparison, everything())
# Filter to keep only the first comparison result (ignoring reversed comparisons)
results_df <- results_df %>%
filter(!grepl("vs", Comparison) | !duplicated(gsub("vs.*", "", Comparison)))
# Create flextable
fancy_table2 <- flextable(results_df)
fancy_table2 <- set_table_properties(fancy_table2, width = 0.8, layout = "autofit")
fancy_table2 <- set_caption(fancy_table2, caption = "Diagnosis of new categorical endpoints compared to case control status (gold standard) after excluding NCSU (N=173)")
fancy_table2 <- fontsize(fancy_table2, size = 8, part = "all") # Reduce font size of values
fancy_table2 <- fontsize(fancy_table2, size = 9, part = "header") # Reduce font size of header
# Print flextable
print(fancy_table2)## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa`
## header has 1 row(s)
## body has 5 row(s)
## original dataset sample:
## Comparison
## lowest cut ast vs cirrhosis diagnosed lowest cut ast vs cirrhosis diagnosed
## highest cut ast vs cirrhosis diagnosed highest cut ast vs cirrhosis diagnosed
## lowest cut alt vs cirrhosis diagnosed lowest cut alt vs cirrhosis diagnosed
## highest cut alt vs cirrhosis diagnosed highest cut alt vs cirrhosis diagnosed
## cirrhosis vs cirrhosis diagnosed cirrhosis vs cirrhosis diagnosed
## Sensitivity Specificity PPV NPV
## lowest cut ast vs cirrhosis diagnosed 0.029 1 1 0.514
## highest cut ast vs cirrhosis diagnosed 0.029 0.990 0.500 0.754
## lowest cut alt vs cirrhosis diagnosed 0 0.985 0 0.942
## highest cut alt vs cirrhosis diagnosed 0 0.985 0 0.964
## cirrhosis vs cirrhosis diagnosed 0.015 1 1 0.022
## Accuracy F1_Score Kappa
## lowest cut ast vs cirrhosis diagnosed 0.521 0.056 0
## highest cut ast vs cirrhosis diagnosed 0.750 0.054 0
## lowest cut alt vs cirrhosis diagnosed 0.929 NA 0
## highest cut alt vs cirrhosis diagnosed 0.950 NA 0
## cirrhosis vs cirrhosis diagnosed 0.036 0.029 0###
doc <- read_docx()
doc <- body_add_flextable(doc, fancy_table)
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_flextable(doc, fancy_table1)
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_flextable(doc, fancy_table2)
print(doc, target = "Categorical endpoint diagnosis.docx")
############### scatter plot
results_df <- results_df %>% dplyr::select(-Kappa)
results_long <- results_df %>%
pivot_longer(cols = -Comparison, names_to = "Metric", values_to = "Value")
results_long <- results_long %>%
mutate(Value = as.numeric(Value)) %>%
filter(!is.na(Value)) ## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Value = as.numeric(Value)`.
## Caused by warning:
## ! NAs introduced by coercionnew_labels <- c(
"cirrhosis vs cirrhosis diagnosed" = "cirrhosis by ast/alt>1",
"highest cut alt vs cirrhosis diagnosed" = "highest cut alt",
"lowest cut alt vs cirrhosis diagnosed" = "lowest cut alt",
"highest cut ast vs cirrhosis diagnosed" = "highest cut ast",
"lowest cut ast vs cirrhosis diagnosed" = "lowest cut ast"
)
palette_colors <- brewer.pal(n = length(unique(results_long$Comparison)), name = "Set1")
# Recode the Comparison column
results_long <- results_long %>%
mutate(Comparison = recode(Comparison, !!!new_labels))
# Create the scatter plot
ggplot(results_long, aes(x = Value, y = Metric, color = Comparison, shape = Comparison)) +
geom_point(size = 3) +
labs(title = "Diagnostic testing after excluding NCSU",
x = "Scores",
y = "Measure of diagnostic test performance",
color = "Comparison",
shape = "Comparison") +
scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1)) + # Adjust x-axis
theme(axis.text.y = element_text(size = 10),
axis.text.x = element_text(size = 10),
axis.title = element_text(size = 12),
legend.position = "right") +
scale_shape_manual(values = 1:length(unique(results_long$Comparison))) +
scale_color_manual(values = palette_colors)
Correlation heat map btw PFAS and potential confounders
# reset up reference category of some potential confounders
vars_to_convert <- c("source", "race_final_label","sq_drink_alcohol","sq_average_drink_per_day")
data[vars_to_convert] <- lapply(data[vars_to_convert], as.factor)
data$source <- relevel(data$source, ref = "Emory")
data$race_final_label <- relevel(data$race_final_label, ref = "White")
data$sq_drink_alcohol <- relevel(data$sq_drink_alcohol, ref = "No, never drinker")
data$sq_average_drink_per_day <- relevel(data$sq_average_drink_per_day, ref = "Less than 1 alcoholic drink per day")
# Define continuous potential confounders
numeric_data <- data %>%
dplyr::select(all_of(potential_conf)) %>%
dplyr::select_if(is.numeric)
conti_conf <- names(numeric_data)
# Function to extract model summary for continuous covariates
extract_model_summary <- function(pfas, covariate, data) {
model <- lm(as.formula(paste(pfas, "~", covariate)), data = data)
summary_model <- summary(model)
estimate <- coef(summary_model)[2, 1]
p_value <- coef(summary_model)[2, 4]
return(data.frame(Confounders = covariate, Coeff = estimate, P = p_value, Factor = covariate, PFAS = pfas))
}
# Apply to all PFAS variables and continuous covariates
continuous_results <- bind_rows(lapply(pfas_name_scld, function(pfas) {
bind_rows(lapply(conti_conf, function(cov) extract_model_summary(pfas, cov, data)))
}))
# Print continuous results
print(continuous_results)## Confounders Coeff P Factor
## 1 age_at_enrollment 0.02303963456 0.00002886969906048 age_at_enrollment
## 2 bmi -0.01294812141 0.09611802745084295 bmi
## 3 trig_mg_d_l 0.00006026119 0.92894071463283046 trig_mg_d_l
## 4 age_at_enrollment 0.01401689114 0.01151280974826796 age_at_enrollment
## 5 bmi -0.01953451965 0.01049324625441954 bmi
## 6 trig_mg_d_l -0.00087134772 0.19560135743360868 trig_mg_d_l
## 7 age_at_enrollment 0.02285242310 0.00003418692654294 age_at_enrollment
## 8 bmi -0.01016268100 0.19162630103330469 bmi
## 9 trig_mg_d_l -0.00012960498 0.84543151201780353 trig_mg_d_l
## 10 age_at_enrollment 0.03015972790 0.00000003849922364 age_at_enrollment
## 11 bmi -0.00632410823 0.39797483121906918 bmi
## 12 trig_mg_d_l -0.00060423845 0.36564464944825859 trig_mg_d_l
## 13 age_at_enrollment 0.00610856935 0.27012846280853531 age_at_enrollment
## 14 bmi -0.00353210464 0.63621285405847949 bmi
## 15 trig_mg_d_l -0.00037474559 0.57810255513241993 trig_mg_d_l
## 16 age_at_enrollment -0.00039411970 0.94418956366096030 age_at_enrollment
## 17 bmi 0.00294487490 0.67943161804413910 bmi
## 18 trig_mg_d_l -0.00051069442 0.45289086346786955 trig_mg_d_l
## 19 age_at_enrollment 0.01990379953 0.00032706545799936 age_at_enrollment
## 20 bmi -0.01850220830 0.01730193122393180 bmi
## 21 trig_mg_d_l 0.00025406806 0.70465471480120978 trig_mg_d_l
## 22 age_at_enrollment 0.00422728729 0.41715088028540981 age_at_enrollment
## 23 bmi -0.00405085209 0.60753830998090086 bmi
## 24 trig_mg_d_l -0.00053365608 0.39127771559889879 trig_mg_d_l
## 25 age_at_enrollment 0.01474075004 0.00829630670610244 age_at_enrollment
## 26 bmi -0.02788390573 0.00016089041529066 bmi
## 27 trig_mg_d_l -0.00105386273 0.11863558257777872 trig_mg_d_l
## 28 age_at_enrollment 0.03647003290 0.00000000001254972 age_at_enrollment
## 29 bmi -0.00498536122 0.49783998070122160 bmi
## 30 trig_mg_d_l 0.00035106604 0.59487932766741591 trig_mg_d_l
## 31 age_at_enrollment 0.00666942172 0.23478386028671305 age_at_enrollment
## 32 bmi -0.02691555479 0.00053014713321087 bmi
## 33 trig_mg_d_l -0.00088518107 0.19110820055049224 trig_mg_d_l
## 34 age_at_enrollment 0.00723125430 0.18396989965273475 age_at_enrollment
## 35 bmi -0.02551872568 0.00081610677927414 bmi
## 36 trig_mg_d_l -0.00011850725 0.86073672780481414 trig_mg_d_l
## 37 age_at_enrollment -0.00767217344 0.16771220778328327 age_at_enrollment
## 38 bmi 0.00258183929 0.73804930867047069 bmi
## 39 trig_mg_d_l -0.00082944038 0.21370329383697809 trig_mg_d_l
## 40 age_at_enrollment 0.00156141004 0.75969057121585326 age_at_enrollment
## 41 bmi -0.00261010632 0.73748118274195495 bmi
## 42 trig_mg_d_l -0.00015137036 0.80919075133267548 trig_mg_d_l
## PFAS
## 1 pf_hx_s_scld
## 2 pf_hx_s_scld
## 3 pf_hx_s_scld
## 4 pfda_scld
## 5 pfda_scld
## 6 pfda_scld
## 7 pfna_scld
## 8 pfna_scld
## 9 pfna_scld
## 10 pfos_scld
## 11 pfos_scld
## 12 pfos_scld
## 13 pf_hp_a_scld
## 14 pf_hp_a_scld
## 15 pf_hp_a_scld
## 16 pfbs_scld
## 17 pfbs_scld
## 18 pfbs_scld
## 19 pfoa_scld
## 20 pfoa_scld
## 21 pfoa_scld
## 22 pf_pe_a_scld
## 23 pf_pe_a_scld
## 24 pf_pe_a_scld
## 25 pf_un_a_scld
## 26 pf_un_a_scld
## 27 pf_un_a_scld
## 28 pf_hp_s_scld
## 29 pf_hp_s_scld
## 30 pf_hp_s_scld
## 31 pf_do_a_scld
## 32 pf_do_a_scld
## 33 pf_do_a_scld
## 34 pf_pe_s_scld
## 35 pf_pe_s_scld
## 36 pf_pe_s_scld
## 37 pf_hx_a_scld
## 38 pf_hx_a_scld
## 39 pf_hx_a_scld
## 40 pfba_scld
## 41 pfba_scld
## 42 pfba_scld# Define categorical confounders
cate_conf <- setdiff(potential_conf, conti_conf)
data <- data %>%
mutate(across(all_of(cate_conf), as.factor))
# Function to fit model and handle categorical variables
fit_model_cat <- function(pfas, covariate, data) {
model <- lm(as.formula(paste(pfas, "~", covariate)), data = data)
summary_model <- summary(model)
coefficients_df <- as.data.frame(summary_model$coefficients)
coefficients_df <- coefficients_df[-1, ] # Exclude intercept
results <- data.frame()
for (i in 1:nrow(coefficients_df)) {
current_result <- data.frame(
Confounders = covariate,
Coeff = coefficients_df[i, "Estimate"],
P = coefficients_df[i, "Pr(>|t|)"],
Factor = rownames(coefficients_df)[i],
PFAS = pfas
)
results <- rbind(results, current_result)
}
return(results)
}
# Apply to all PFAS variables and categorical covariates
categorical_results <- bind_rows(lapply(pfas_name_scld, function(pfas) {
bind_rows(lapply(cate_conf, function(cov) fit_model_cat(pfas, cov, data)))
}))
# Print categorical results
print(categorical_results)## Confounders Coeff P
## 1 source 0.200895798 0.17816637785406
## 2 source 0.287731004 0.02406241741592
## 3 source -0.240398916 0.34812012829126
## 4 sex 0.187197691 0.08219582251875
## 5 race_eth_label 0.050788492 0.84233629638901
## 6 race_eth_label -0.115104272 0.74149107535182
## 7 race_eth_label 0.274229818 0.26292213105599
## 8 race_eth_label 0.038885809 0.89888254812528
## 9 race_final_label -0.125411971 0.86037830965616
## 10 race_final_label -0.567654328 0.26261635127197
## 11 race_final_label -0.094110264 0.87184652297268
## 12 race_final_label -0.407973324 0.32604479871633
## 13 race_final_label -0.209886202 0.07137795642001
## 14 race_final_label -0.264366169 0.79275654669035
## 15 race_final_label -0.293394272 0.32387441802348
## 16 race_final_label -0.140730470 0.71483775593415
## 17 ethnicity 0.189177168 0.43536968718843
## 18 ethnicity 0.038885809 0.89927233218110
## 19 rural -0.246551067 0.18530426138296
## 20 rural -0.147877097 0.36453370981274
## 21 smoking -0.323810274 0.07305486987873
## 22 smoking -0.229485806 0.04908826819920
## 23 sq_drink_alcohol -0.091942208 0.55776878080577
## 24 sq_drink_alcohol -0.224849555 0.12238274703819
## 25 sq_drink_alcohol -0.005566960 0.96937817849958
## 26 sq_average_drink_per_day 0.116084716 0.65578352846414
## 27 sq_average_drink_per_day -0.261826993 0.53666952886876
## 28 sq_average_drink_per_day -0.105299660 0.40074255201137
## 29 sq_self_hep_b -0.141938612 0.21157942812386
## 30 sq_self_hep_b 0.192152070 0.44361557003896
## 31 sq_self_hep_c -0.146606869 0.19870154554602
## 32 sq_self_hep_c -0.155239261 0.52527191567200
## 33 supp_meds_tylenol 0.304940955 0.42547659640687
## 34 supp_meds_tylenol 0.419083324 0.50488516530130
## 35 supp_meds_steroids 0.040924100 0.90371151384786
## 36 supp_meds_steroids -0.657053844 0.53425913283768
## 37 sq_water_well -0.209121056 0.06225382264052
## 38 sq_water_well -0.218124485 0.15388307408752
## 39 sq_water_tap_unfiltered -0.023621223 0.86549139364515
## 40 sq_water_tap_unfiltered 0.242930596 0.06779584280842
## 41 sq_water_house_filtration -0.164768323 0.13133740209379
## 42 sq_water_house_filtration -0.015941373 0.93081104646968
## 43 sq_water_faucet_filter -0.137999833 0.25477840057300
## 44 sq_water_faucet_filter 0.058969782 0.65148799314024
## 45 sq_water_charcoal_filter -0.157835307 0.15697464465805
## 46 sq_water_charcoal_filter 0.078120988 0.61742210595572
## 47 sq_water_bottled -0.489245463 0.00049146516071
## 48 sq_water_bottled -0.425652852 0.00165296201584
## 49 sq_water_none -0.155707258 0.14497821238538
## 50 sq_water_none -0.401945941 0.10160522807343
## 51 sq_water_other_type -0.144238597 0.18408036183405
## 52 sq_water_other_type -0.171961692 0.40129154196832
## 53 source -0.246320491 0.09903735645484
## 54 source -0.089689998 0.48062363680237
## 55 source -0.664123896 0.00983867718366
## 56 sex -0.132717021 0.21831847824619
## 57 race_eth_label 0.124713526 0.62644193500231
## 58 race_eth_label 0.427845656 0.22209485749045
## 59 race_eth_label 0.219074045 0.37257706140279
## 60 race_eth_label -0.051398854 0.86705038235471
## 61 race_final_label -0.129041956 0.85548381268167
## 62 race_final_label -0.480227615 0.34013223492180
## 63 race_final_label 0.221625730 0.70227327313975
## 64 race_final_label 0.731311112 0.07689498379755
## 65 race_final_label -0.106015704 0.35862071309513
## 66 race_final_label 1.830449710 0.06776455127017
## 67 race_final_label -0.181875197 0.53812055995121
## 68 race_final_label -0.426284433 0.26576060685672
## 69 ethnicity 0.199915847 0.40915967427517
## 70 ethnicity -0.051398854 0.86694944980858
## 71 rural -0.223213006 0.23079719614026
## 72 rural -0.089919293 0.58161444509190
## 73 smoking -0.477786491 0.00796269280740
## 74 smoking -0.268518439 0.02067734372000
## 75 sq_drink_alcohol -0.207870279 0.17690069057810
## 76 sq_drink_alcohol -0.133574488 0.34875449247860
## 77 sq_drink_alcohol 0.343071427 0.01623256250437
## 78 sq_average_drink_per_day -0.065819563 0.79640617614151
## 79 sq_average_drink_per_day 0.309710825 0.45570924404055
## 80 sq_average_drink_per_day -0.449821686 0.00027963169345
## 81 sq_self_hep_b -0.195178728 0.08568444046719
## 82 sq_self_hep_b 0.124792657 0.61823627484439
## 83 sq_self_hep_c -0.188575571 0.09718837256862
## 84 sq_self_hep_c 0.293829902 0.22767940372033
## 85 supp_meds_tylenol -0.104426492 0.78494873278750
## 86 supp_meds_tylenol 0.137188835 0.82724883059650
## 87 supp_meds_steroids -0.209622339 0.53564564076427
## 88 supp_meds_steroids -0.222383053 0.83337724859734
## 89 sq_water_well -0.132687198 0.23787633145928
## 90 sq_water_well -0.097902158 0.52323933577829
## 91 sq_water_tap_unfiltered -0.071825166 0.60904913514096
## 92 sq_water_tap_unfiltered 0.047090239 0.72458169476826
## 93 sq_water_house_filtration -0.118485504 0.27832139702085
## 94 sq_water_house_filtration -0.069280874 0.70639209383939
## 95 sq_water_faucet_filter -0.111215023 0.35955630258943
## 96 sq_water_faucet_filter -0.003617243 0.97793718366327
## 97 sq_water_charcoal_filter -0.118284545 0.28976269305199
## 98 sq_water_charcoal_filter -0.030459708 0.84593988600084
## 99 sq_water_bottled -0.338884188 0.01623333367883
## 100 sq_water_bottled -0.326239577 0.01651137591491
## 101 sq_water_none -0.081531902 0.44683501274427
## 102 sq_water_none -0.161168516 0.51271635885510
## 103 sq_water_other_type -0.140356692 0.19632346742509
## 104 sq_water_other_type 0.005741845 0.97764091282822
## 105 source -0.175369787 0.24029175304655
## 106 source -0.001557516 0.99023454642434
## 107 source -0.604201367 0.01887733932903
## 108 sex -0.182491531 0.09022350808633
## 109 race_eth_label 0.111439098 0.66399374875135
## 110 race_eth_label 0.075383541 0.82971361421867
## 111 race_eth_label 0.235158486 0.33914499829761
## 112 race_eth_label 0.010202957 0.97352278690987
## 113 race_final_label -0.031005242 0.96535916817741
## 114 race_final_label -0.439367404 0.38636569597660
## 115 race_final_label -0.010423415 0.98576358357417
## 116 race_final_label -0.117074701 0.77822396547546
## 117 race_final_label -0.118282395 0.30947290327306
## 118 race_final_label 1.415335888 0.16063322156114
## 119 race_final_label -0.232301749 0.43522678347209
## 120 race_final_label -0.250188337 0.51661750631772
## 121 ethnicity 0.190570814 0.43185377958703
## 122 ethnicity 0.010202957 0.97349654239088
## 123 rural -0.166572585 0.37123860525779
## 124 rural -0.122277328 0.45397511461728
## 125 smoking -0.550497166 0.00222769302932
## 126 smoking -0.243033617 0.03579987987392
## 127 sq_drink_alcohol -0.152143531 0.32289373258877
## 128 sq_drink_alcohol -0.064578736 0.65051917779038
## 129 sq_drink_alcohol 0.395101987 0.00571563030052
## 130 sq_average_drink_per_day -0.179363348 0.48139369719177
## 131 sq_average_drink_per_day 0.339756072 0.41243345212406
## 132 sq_average_drink_per_day -0.475665430 0.00012072205421
## 133 sq_self_hep_b -0.169980812 0.13479409364304
## 134 sq_self_hep_b 0.077652727 0.75677422534222
## 135 sq_self_hep_c -0.153241706 0.17920855998162
## 136 sq_self_hep_c -0.041526636 0.86504317800144
## 137 supp_meds_tylenol 0.137831746 0.71761513785548
## 138 supp_meds_tylenol 1.045530092 0.09550981098713
## 139 supp_meds_steroids -0.308992929 0.36097388049451
## 140 supp_meds_steroids 0.101453855 0.92347692411989
## 141 sq_water_well -0.197910956 0.07794860503605
## 142 sq_water_well -0.145051446 0.34325528210842
## 143 sq_water_tap_unfiltered -0.066858483 0.63379349809383
## 144 sq_water_tap_unfiltered 0.077746821 0.56054814450977
## 145 sq_water_house_filtration -0.157196639 0.15011332667990
## 146 sq_water_house_filtration -0.063597366 0.72917960795944
## 147 sq_water_faucet_filter -0.167657482 0.16701503357658
## 148 sq_water_faucet_filter -0.062872816 0.63038370929638
## 149 sq_water_charcoal_filter -0.171482905 0.12453939373143
## 150 sq_water_charcoal_filter -0.088273734 0.57281724091754
## 151 sq_water_bottled -0.316950714 0.02481255320483
## 152 sq_water_bottled -0.257416023 0.05871655083207
## 153 sq_water_none -0.150395243 0.15986807660113
## 154 sq_water_none -0.280592971 0.25347435646347
## 155 sq_water_other_type -0.178793168 0.09958577804326
## 156 sq_water_other_type -0.063262997 0.75719660380251
## 157 source -0.028123983 0.85071911056610
## 158 source 0.065459503 0.60757504689612
## 159 source -0.549343879 0.03293039529831
## 160 sex 0.170180187 0.11421477651813
## 161 race_eth_label 0.177351427 0.48947961492581
## 162 race_eth_label 0.213688811 0.54222117666899
## 163 race_eth_label 0.220729261 0.36962368798292
## 164 race_eth_label -0.064254677 0.83445298522021
## 165 race_final_label -0.256501601 0.71719909410729
## 166 race_final_label -0.612124024 0.22393436309560
## 167 race_final_label 0.302218261 0.60200810793988
## 168 race_final_label 0.345060629 0.40285038365019
## 169 race_final_label -0.030336969 0.79256603065347
## 170 race_final_label 2.506331197 0.01250591825764
## 171 race_final_label -0.261501159 0.37590551468125
## 172 race_final_label -0.356932287 0.35102751784317
## 173 ethnicity 0.207295824 0.39191644876826
## 174 ethnicity -0.064254677 0.83404410390463
## 175 rural -0.150503790 0.41945767850033
## 176 rural -0.025677499 0.87510243365735
## 177 smoking -0.568161472 0.00162483462452
## 178 smoking -0.177642837 0.12484388703393
## 179 sq_drink_alcohol -0.092797903 0.55221901412800
## 180 sq_drink_alcohol -0.036172572 0.80250707464715
## 181 sq_drink_alcohol 0.250989822 0.08264764890084
## 182 sq_average_drink_per_day -0.147331304 0.56878498930872
## 183 sq_average_drink_per_day 0.137412767 0.74388410618384
## 184 sq_average_drink_per_day -0.309054336 0.01330787645467
## 185 sq_self_hep_b -0.096081724 0.39765419685309
## 186 sq_self_hep_b 0.273215641 0.27638203097615
## 187 sq_self_hep_c -0.088278490 0.43852931773111
## 188 sq_self_hep_c 0.266319451 0.27592232776750
## 189 supp_meds_tylenol 0.222254099 0.56129273508639
## 190 supp_meds_tylenol 0.490187456 0.43550301353661
## 191 supp_meds_steroids -0.001370466 0.99676971448587
## 192 supp_meds_steroids -0.083001213 0.93744563737665
## 193 sq_water_well -0.138214358 0.21869101547987
## 194 sq_water_well -0.146054748 0.34083898263585
## 195 sq_water_tap_unfiltered -0.108841289 0.43862900238366
## 196 sq_water_tap_unfiltered -0.031520281 0.81362118915628
## 197 sq_water_house_filtration -0.101441951 0.35332742478950
## 198 sq_water_house_filtration 0.028812444 0.87552600125445
## 199 sq_water_faucet_filter -0.144511349 0.23371706850435
## 200 sq_water_faucet_filter -0.115764438 0.37605746039475
## 201 sq_water_charcoal_filter -0.094357710 0.39811618261251
## 202 sq_water_charcoal_filter 0.078374853 0.61705027014983
## 203 sq_water_bottled -0.374389594 0.00776080774018
## 204 sq_water_bottled -0.398004138 0.00339692534565
## 205 sq_water_none -0.097152295 0.36444330562043
## 206 sq_water_none -0.221344056 0.36842362123646
## 207 sq_water_other_type -0.118651822 0.27482381292786
## 208 sq_water_other_type -0.128580014 0.53059508137586
## 209 source 0.104342436 0.48142224323953
## 210 source -0.253170703 0.04569477728083
## 211 source 0.366718557 0.15030326977520
## 212 sex 0.047539722 0.65952777755573
## 213 race_eth_label -0.292172691 0.24558725047750
## 214 race_eth_label 0.259040647 0.45098650537994
## 215 race_eth_label 0.197393303 0.41292192888075
## 216 race_eth_label -0.004429835 0.98826938151728
## 217 race_final_label 2.990353342 0.00001177078990
## 218 race_final_label -0.750763195 0.11711937574190
## 219 race_final_label -0.213625556 0.69833970735644
## 220 race_final_label -0.201060933 0.60825038534995
## 221 race_final_label -0.528106273 0.00000214067538
## 222 race_final_label 0.144466203 0.87920452393557
## 223 race_final_label -0.541876335 0.05422224390061
## 224 race_final_label -0.641720215 0.07841348875071
## 225 ethnicity 0.052119485 0.82999524103982
## 226 ethnicity -0.004429835 0.98850968884498
## 227 rural 0.039818426 0.83089199323123
## 228 rural 0.067323679 0.68047191960674
## 229 smoking -0.258608572 0.15406242068696
## 230 smoking -0.042662611 0.71521597801004
## 231 sq_drink_alcohol 0.135862805 0.38818440115655
## 232 sq_drink_alcohol 0.048099616 0.74153340009344
## 233 sq_drink_alcohol 0.065602936 0.65217578709575
## 234 sq_average_drink_per_day 0.343582611 0.18358567488179
## 235 sq_average_drink_per_day 1.133059764 0.00724023699402
## 236 sq_average_drink_per_day 0.112919552 0.36326518486072
## 237 sq_self_hep_b -0.005245175 0.96324302106208
## 238 sq_self_hep_b -0.157051829 0.53229219046350
## 239 sq_self_hep_c 0.076666646 0.50144498984546
## 240 sq_self_hep_c 0.256164632 0.29506967023342
## 241 supp_meds_tylenol 0.142366243 0.70995004114878
## 242 supp_meds_tylenol 0.095580883 0.87917492730683
## 243 supp_meds_steroids 0.106160935 0.75379520356563
## 244 supp_meds_steroids -0.006282312 0.99525978948955
## 245 sq_water_well -0.079017038 0.48242764822963
## 246 sq_water_well -0.029840353 0.84588876056705
## 247 sq_water_tap_unfiltered 0.039163075 0.78055819320376
## 248 sq_water_tap_unfiltered 0.059596919 0.65602092727267
## 249 sq_water_house_filtration -0.064832914 0.55318487403520
## 250 sq_water_house_filtration 0.044680352 0.80820617839498
## 251 sq_water_faucet_filter 0.045347249 0.70845488436321
## 252 sq_water_faucet_filter 0.153061672 0.24212063939652
## 253 sq_water_charcoal_filter -0.087033046 0.43594263519055
## 254 sq_water_charcoal_filter -0.132945406 0.39671140672906
## 255 sq_water_bottled -0.162599041 0.25016970657883
## 256 sq_water_bottled -0.218302665 0.10999177430940
## 257 sq_water_none -0.052129747 0.62687470346805
## 258 sq_water_none 0.041283762 0.86689577335487
## 259 sq_water_other_type -0.084331471 0.43780014503922
## 260 sq_water_other_type 0.059851386 0.77047252115802
## 261 source -0.242092835 0.10550138874091
## 262 source -0.314762573 0.01381394218896
## 263 source -0.438709022 0.08785424567301
## 264 sex -0.074198882 0.49160923227275
## 265 race_eth_label -0.014642162 0.95432754792565
## 266 race_eth_label -0.023386604 0.94662591167007
## 267 race_eth_label 0.216205940 0.37788131612548
## 268 race_eth_label 0.294469251 0.33681006421286
## 269 race_final_label -0.064977808 0.92729153707793
## 270 race_final_label -0.364982301 0.47055950006093
## 271 race_final_label -0.287203346 0.62210940950523
## 272 race_final_label -0.234821601 0.57120995522041
## 273 race_final_label -0.261517532 0.02465261727798
## 274 race_final_label -0.322124510 0.74855511160560
## 275 race_final_label -0.276092067 0.35256225658707
## 276 race_final_label -0.334369593 0.38492197181345
## 277 ethnicity 0.135638436 0.57595190803645
## 278 ethnicity 0.294469251 0.33814572280042
## 279 rural 0.402235775 0.03046477151052
## 280 rural -0.025462981 0.87544973896638
## 281 smoking 0.028762081 0.87397920554473
## 282 smoking -0.132220016 0.25890118865790
## 283 sq_drink_alcohol 0.030936089 0.84399176612941
## 284 sq_drink_alcohol -0.113543959 0.43588771007607
## 285 sq_drink_alcohol 0.042489490 0.77007949378540
## 286 sq_average_drink_per_day 0.001738499 0.99459668702332
## 287 sq_average_drink_per_day 1.434035309 0.00065732225553
## 288 sq_average_drink_per_day 0.001408930 0.99089640548375
## 289 sq_self_hep_b -0.161647304 0.15490547435844
## 290 sq_self_hep_b -0.231096311 0.35688641562230
## 291 sq_self_hep_c -0.110620492 0.33226578512771
## 292 sq_self_hep_c 0.117368598 0.63123268162753
## 293 supp_meds_tylenol 0.051114225 0.89376908860934
## 294 supp_meds_tylenol -0.100001513 0.87363945925546
## 295 supp_meds_steroids 0.067647998 0.84159227516948
## 296 supp_meds_steroids -0.135451785 0.89808056927625
## 297 sq_water_well 0.028668599 0.79878534040690
## 298 sq_water_well -0.083682739 0.58576338919295
## 299 sq_water_tap_unfiltered -0.048786994 0.72768354490861
## 300 sq_water_tap_unfiltered -0.196265781 0.14160342074545
## 301 sq_water_house_filtration 0.066166986 0.54472537125353
## 302 sq_water_house_filtration 0.183741850 0.31807308447128
## 303 sq_water_faucet_filter 0.084888011 0.48456664213754
## 304 sq_water_faucet_filter 0.059173337 0.65126163583327
## 305 sq_water_charcoal_filter 0.061960093 0.57934760076695
## 306 sq_water_charcoal_filter 0.086905004 0.57974234090502
## 307 sq_water_bottled 0.001506774 0.99150577538440
## 308 sq_water_bottled -0.113192372 0.40754821004133
## 309 sq_water_none 0.033612442 0.75393115719871
## 310 sq_water_none -0.083659173 0.73415922696711
## 311 sq_water_other_type 0.024303777 0.82315529229055
## 312 sq_water_other_type -0.047333276 0.81768277140598
## 313 source -0.076284269 0.61053968426051
## 314 source 0.069320160 0.58737751970846
## 315 source -0.428567690 0.09643431102686
## 316 sex -0.125164803 0.24571033496868
## 317 race_eth_label -0.149446479 0.55373366719946
## 318 race_eth_label 0.138177043 0.68861553399695
## 319 race_eth_label 0.298831816 0.21707705491636
## 320 race_eth_label 0.014561956 0.96159120427071
## 321 race_final_label 0.233611347 0.73943226114685
## 322 race_final_label -0.682571588 0.17165331000666
## 323 race_final_label 0.067011955 0.90716829723056
## 324 race_final_label 0.012995087 0.97464817278574
## 325 race_final_label -0.430966705 0.00019045927418
## 326 race_final_label 0.542360390 0.58429976766167
## 327 race_final_label -0.300302745 0.30534074418753
## 328 race_final_label 0.025747942 0.94588993724285
## 329 ethnicity 0.156059572 0.51999177754676
## 330 ethnicity 0.014561956 0.96220473628701
## 331 rural -0.190148486 0.30702073717271
## 332 rural -0.170630017 0.29578828788987
## 333 smoking -0.409788834 0.02321464375482
## 334 smoking -0.222632007 0.05574471466387
## 335 sq_drink_alcohol -0.111496089 0.46913030842951
## 336 sq_drink_alcohol -0.046732994 0.74326861358174
## 337 sq_drink_alcohol 0.409557468 0.00422640049175
## 338 sq_average_drink_per_day -0.040424653 0.87408657104043
## 339 sq_average_drink_per_day -0.260093248 0.53096833080426
## 340 sq_average_drink_per_day -0.481474629 0.00010257804470
## 341 sq_self_hep_b -0.178851753 0.11559742974388
## 342 sq_self_hep_b 0.045228192 0.85681327359605
## 343 sq_self_hep_c -0.177866607 0.11880220685087
## 344 sq_self_hep_c 0.005156484 0.98314670068817
## 345 supp_meds_tylenol 0.443801098 0.24563690847941
## 346 supp_meds_tylenol 0.770507972 0.21985255079337
## 347 supp_meds_steroids 0.107805082 0.75003621633081
## 348 supp_meds_steroids -0.380809440 0.71869901424882
## 349 sq_water_well -0.186536945 0.09675928899761
## 350 sq_water_well -0.112239398 0.46350632851723
## 351 sq_water_tap_unfiltered -0.007174993 0.95913876245625
## 352 sq_water_tap_unfiltered 0.175492994 0.18845245498274
## 353 sq_water_house_filtration -0.160648035 0.14111864812130
## 354 sq_water_house_filtration -0.184146789 0.31607063806375
## 355 sq_water_faucet_filter -0.180991093 0.13559656249832
## 356 sq_water_faucet_filter -0.144357201 0.26925087582704
## 357 sq_water_charcoal_filter -0.171264179 0.12499948414919
## 358 sq_water_charcoal_filter -0.044223355 0.77750970889268
## 359 sq_water_bottled -0.437282541 0.00182086074127
## 360 sq_water_bottled -0.459621966 0.00069540024129
## 361 sq_water_none -0.165431763 0.12150146864344
## 362 sq_water_none -0.392843913 0.10950059241962
## 363 sq_water_other_type -0.181037121 0.09537905447064
## 364 sq_water_other_type -0.037666105 0.85393082686463
## 365 source -0.597901038 0.00005228868616
## 366 source -0.265570259 0.03370576474755
## 367 source 0.326611981 0.19403427471931
## 368 sex -0.153686852 0.15386599131094
## 369 race_eth_label 0.072315457 0.77714097293777
## 370 race_eth_label 0.474258658 0.17483025510466
## 371 race_eth_label 0.086984175 0.72246058781372
## 372 race_eth_label 0.457678603 0.13554981673754
## 373 race_final_label 0.347296961 0.62303943070383
## 374 race_final_label 1.652976248 0.00107063992872
## 375 race_final_label -0.072385667 0.90034178273734
## 376 race_final_label -0.041298065 0.92002234501068
## 377 race_final_label 0.062986709 0.58422548881022
## 378 race_final_label -0.258911280 0.79509259208138
## 379 race_final_label -0.207098635 0.48201712804661
## 380 race_final_label -0.227823677 0.55058320024782
## 381 ethnicity 0.099940992 0.67927159271312
## 382 ethnicity 0.457678603 0.13566211156982
## 383 rural -0.147231442 0.42822635251512
## 384 rural -0.260727617 0.11000532282996
## 385 smoking 0.216112013 0.23338823688727
## 386 smoking -0.066187762 0.57137978178832
## 387 sq_drink_alcohol 0.101568743 0.51822776011725
## 388 sq_drink_alcohol -0.001902538 0.98957692131135
## 389 sq_drink_alcohol 0.151438614 0.29776677122813
## 390 sq_average_drink_per_day 0.057487391 0.82519940509489
## 391 sq_average_drink_per_day -0.344924347 0.41554963544340
## 392 sq_average_drink_per_day -0.132431733 0.29051139953655
## 393 sq_self_hep_b -0.127726808 0.26138986317014
## 394 sq_self_hep_b -0.189266149 0.45099558920554
## 395 sq_self_hep_c -0.124364032 0.27562127705554
## 396 sq_self_hep_c -0.191670281 0.43308498300754
## 397 supp_meds_tylenol 0.259503283 0.49765332661943
## 398 supp_meds_tylenol 0.077719005 0.90156876480205
## 399 supp_meds_steroids 0.217854838 0.51959775927010
## 400 supp_meds_steroids -0.290150953 0.78364104048900
## 401 sq_water_well -0.035396466 0.75308976935880
## 402 sq_water_well -0.004037423 0.97903005717147
## 403 sq_water_tap_unfiltered -0.227639599 0.10460564698569
## 404 sq_water_tap_unfiltered -0.209658682 0.11625097875513
## 405 sq_water_house_filtration 0.031800030 0.76900375265055
## 406 sq_water_house_filtration 0.499637112 0.00639538693209
## 407 sq_water_faucet_filter -0.007319188 0.95193128359767
## 408 sq_water_faucet_filter 0.073005971 0.57712410414268
## 409 sq_water_charcoal_filter -0.026450287 0.81297502605541
## 410 sq_water_charcoal_filter 0.038558107 0.80596269263471
## 411 sq_water_bottled -0.128076641 0.36598943670439
## 412 sq_water_bottled -0.100027498 0.46439951786495
## 413 sq_water_none -0.001346337 0.98995994083385
## 414 sq_water_none -0.333071106 0.17591982687334
## 415 sq_water_other_type 0.029917853 0.78311655900997
## 416 sq_water_other_type 0.149328289 0.46695697882131
## 417 source -0.122335675 0.40741994614468
## 418 source 0.166651022 0.18606871857654
## 419 source -0.710857596 0.00529967763418
## 420 sex -0.081072838 0.45234435016076
## 421 race_eth_label 0.388436412 0.12719466758624
## 422 race_eth_label 0.754963047 0.03031047612560
## 423 race_eth_label 0.309387448 0.20479634375197
## 424 race_eth_label -0.069642049 0.81923914578154
## 425 race_final_label -0.542170032 0.43999328372661
## 426 race_final_label -0.494735976 0.32117620165951
## 427 race_final_label 0.319841767 0.57762843834789
## 428 race_final_label 1.390329246 0.00073512345399
## 429 race_final_label 0.081404362 0.47660773266545
## 430 race_final_label 0.533002293 0.59053852024601
## 431 race_final_label -0.225942878 0.44010936421998
## 432 race_final_label -0.560240155 0.14005245576398
## 433 ethnicity 0.353302356 0.14289994452075
## 434 ethnicity -0.069642049 0.81947623415182
## 435 rural -0.210790739 0.25793964453799
## 436 rural -0.047194429 0.77247965322541
## 437 smoking -0.559380006 0.00181874007454
## 438 smoking -0.320177642 0.00560773476027
## 439 sq_drink_alcohol -0.177862327 0.24777442714208
## 440 sq_drink_alcohol -0.166639376 0.24258750496615
## 441 sq_drink_alcohol 0.340041634 0.01719459843916
## 442 sq_average_drink_per_day 0.167092869 0.51266621906040
## 443 sq_average_drink_per_day 0.035136353 0.93254748917460
## 444 sq_average_drink_per_day -0.427661820 0.00054676111079
## 445 sq_self_hep_b -0.227855130 0.04436660508740
## 446 sq_self_hep_b 0.243875526 0.32895313970868
## 447 sq_self_hep_c -0.231106095 0.04207291408647
## 448 sq_self_hep_c 0.220640012 0.36434293822168
## 449 supp_meds_tylenol -0.324432302 0.39565811530909
## 450 supp_meds_tylenol 0.265279446 0.67233383417726
## 451 supp_meds_steroids -0.436901492 0.19622409137501
## 452 supp_meds_steroids -0.021862618 0.98346705424688
## 453 sq_water_well -0.128171207 0.25427262322351
## 454 sq_water_well -0.024950983 0.87074988658916
## 455 sq_water_tap_unfiltered -0.222055106 0.11348990546405
## 456 sq_water_tap_unfiltered -0.070017536 0.59948647097833
## 457 sq_water_house_filtration -0.132638341 0.22481218952721
## 458 sq_water_house_filtration -0.039782055 0.82866854646979
## 459 sq_water_faucet_filter -0.131448800 0.27842603953197
## 460 sq_water_faucet_filter 0.029760120 0.81982321014451
## 461 sq_water_charcoal_filter -0.114798173 0.30392858519815
## 462 sq_water_charcoal_filter 0.052864139 0.73582611255727
## 463 sq_water_bottled -0.351141936 0.01283108050418
## 464 sq_water_bottled -0.281397807 0.03857335714817
## 465 sq_water_none -0.096455729 0.36794161779746
## 466 sq_water_none -0.209125261 0.39547290349843
## 467 sq_water_other_type -0.139208496 0.20007125981871
## 468 sq_water_other_type -0.078118811 0.70304722582391
## 469 source 0.061680049 0.67844612713382
## 470 source 0.228325732 0.07252736028874
## 471 source -0.456481340 0.07474208092999
## 472 sex 0.303500510 0.00469493718684
## 473 race_eth_label 0.171448227 0.50197497862571
## 474 race_eth_label -0.082866147 0.81223970154000
## 475 race_eth_label 0.359143079 0.14277455985686
## 476 race_eth_label 0.170940815 0.57643705503039
## 477 race_final_label -0.355957031 0.61712705883957
## 478 race_final_label -0.759880711 0.13339352193114
## 479 race_final_label -0.063258723 0.91352122741302
## 480 race_final_label -0.375470252 0.36528543014309
## 481 race_final_label -0.167859238 0.14835335109304
## 482 race_final_label 1.008017086 0.31601277293424
## 483 race_final_label -0.254488284 0.39135877967639
## 484 race_final_label -0.272328943 0.47898862867844
## 485 ethnicity 0.282534341 0.24394937568137
## 486 ethnicity 0.170940815 0.57772753504936
## 487 rural -0.153308591 0.41080517756178
## 488 rural -0.052872710 0.74619014229260
## 489 smoking -0.597431528 0.00092276549008
## 490 smoking -0.121792259 0.29205421769069
## 491 sq_drink_alcohol -0.065354458 0.67660542380994
## 492 sq_drink_alcohol 0.011750535 0.93549031572168
## 493 sq_drink_alcohol 0.208068661 0.15152091369329
## 494 sq_average_drink_per_day 0.015959027 0.95098333148844
## 495 sq_average_drink_per_day 0.063766417 0.88000354530749
## 496 sq_average_drink_per_day -0.216089727 0.08417452030593
## 497 sq_self_hep_b -0.022472789 0.84347580470059
## 498 sq_self_hep_b 0.146603021 0.55989141502025
## 499 sq_self_hep_c -0.028534432 0.80272136125276
## 500 sq_self_hep_c 0.026712887 0.91313950657492
## 501 supp_meds_tylenol 0.302693492 0.42836384684072
## 502 supp_meds_tylenol 0.781438896 0.21353816457676
## 503 supp_meds_steroids -0.105591434 0.75503614022517
## 504 supp_meds_steroids -0.466254373 0.65925938965953
## 505 sq_water_well -0.067772539 0.54651262733605
## 506 sq_water_well -0.155400137 0.31136152736213
## 507 sq_water_tap_unfiltered -0.007275703 0.95872659269935
## 508 sq_water_tap_unfiltered 0.026032274 0.84574417416999
## 509 sq_water_house_filtration -0.036057060 0.74164992928475
## 510 sq_water_house_filtration -0.016420265 0.92895252444545
## 511 sq_water_faucet_filter -0.078388895 0.51835547757239
## 512 sq_water_faucet_filter -0.124962156 0.33973975540600
## 513 sq_water_charcoal_filter -0.042810377 0.70176869616977
## 514 sq_water_charcoal_filter 0.024256103 0.87718241871529
## 515 sq_water_bottled -0.343434053 0.01441692733044
## 516 sq_water_bottled -0.433131801 0.00143238137611
## 517 sq_water_none -0.039586203 0.71147423698222
## 518 sq_water_none -0.320453635 0.19292650115117
## 519 sq_water_other_type -0.044492329 0.68223041086397
## 520 sq_water_other_type -0.166568984 0.41705478475211
## 521 source 0.003415976 0.98179146397575
## 522 source 0.045020692 0.72434393181778
## 523 source -0.512003703 0.04708117779278
## 524 sex -0.023593908 0.82693773382376
## 525 race_eth_label 0.060863687 0.81187103175203
## 526 race_eth_label 0.283568579 0.41725220517750
## 527 race_eth_label 0.170801740 0.48604179615838
## 528 race_eth_label -0.239941118 0.43384263785675
## 529 race_final_label -0.462599423 0.51535911876491
## 530 race_final_label -0.486996060 0.33501616397590
## 531 race_final_label 0.006627957 0.99090900750855
## 532 race_final_label 0.762330953 0.06613992103282
## 533 race_final_label -0.107573068 0.35324429414613
## 534 race_final_label -0.151427581 0.88002788778056
## 535 race_final_label -0.076316098 0.79678214569844
## 536 race_final_label -0.540407962 0.15992207734304
## 537 ethnicity 0.142621379 0.55500838863838
## 538 ethnicity -0.239941118 0.43331808561261
## 539 rural -0.132863570 0.47604978557856
## 540 rural 0.004063870 0.98015694799679
## 541 smoking -0.339253335 0.06089981157603
## 542 smoking -0.160431500 0.16912075431888
## 543 sq_drink_alcohol 0.033696841 0.82844187593956
## 544 sq_drink_alcohol 0.039289037 0.78511449567258
## 545 sq_drink_alcohol 0.377990514 0.00887878150753
## 546 sq_average_drink_per_day -0.146498661 0.56947537328378
## 547 sq_average_drink_per_day -0.006154150 0.98827941703331
## 548 sq_average_drink_per_day -0.377444112 0.00245565678436
## 549 sq_self_hep_b -0.141051588 0.21493465638587
## 550 sq_self_hep_b 0.042106924 0.86677177612830
## 551 sq_self_hep_c -0.099793531 0.37976264387495
## 552 sq_self_hep_c 0.428406768 0.07911871955042
## 553 supp_meds_tylenol -0.241743315 0.52552310304422
## 554 supp_meds_tylenol 0.743002153 0.23522327501678
## 555 supp_meds_steroids -0.218523659 0.51797858027497
## 556 supp_meds_steroids 0.714542748 0.49868186690490
## 557 sq_water_well 0.011686060 0.91721458430984
## 558 sq_water_well 0.126086020 0.41156166219780
## 559 sq_water_tap_unfiltered -0.092200494 0.51184853848188
## 560 sq_water_tap_unfiltered -0.075435399 0.57279182355582
## 561 sq_water_house_filtration -0.011928675 0.91310090818640
## 562 sq_water_house_filtration -0.129770756 0.48093371950682
## 563 sq_water_faucet_filter -0.027285058 0.82219076468471
## 564 sq_water_faucet_filter -0.090228579 0.49076539977289
## 565 sq_water_charcoal_filter 0.036827190 0.74186846476578
## 566 sq_water_charcoal_filter 0.040403136 0.79688719547919
## 567 sq_water_bottled -0.177736099 0.20792533592252
## 568 sq_water_bottled -0.275383208 0.04357849187013
## 569 sq_water_none 0.022297708 0.83524455521135
## 570 sq_water_none -0.129187361 0.59997849432490
## 571 sq_water_other_type -0.060323288 0.57889504928244
## 572 sq_water_other_type 0.092365555 0.65262891764172
## 573 source 0.128461465 0.38687468700135
## 574 source 0.397209096 0.00181891699049
## 575 source 0.041555547 0.87053458291734
## 576 sex 0.081196520 0.45165441374254
## 577 race_eth_label -0.005861819 0.98166500714231
## 578 race_eth_label 0.072383603 0.83548591495917
## 579 race_eth_label 0.279024063 0.25418843077719
## 580 race_eth_label 0.047160447 0.87739425723636
## 581 race_final_label 1.360287326 0.05400990074290
## 582 race_final_label -0.677922478 0.17574960546630
## 583 race_final_label -0.050667294 0.92993187406726
## 584 race_final_label -0.433484494 0.29078243295854
## 585 race_final_label -0.304808265 0.00818424840147
## 586 race_final_label 0.211835891 0.83120895797992
## 587 race_final_label -0.366394274 0.21245914434555
## 588 race_final_label -0.371598373 0.32895717410029
## 589 ethnicity 0.183595676 0.44910052978292
## 590 ethnicity 0.047160447 0.87801175679233
## 591 rural -0.293698353 0.11397432465806
## 592 rural -0.225951890 0.16528201759410
## 593 smoking -0.328121339 0.07025007004598
## 594 smoking -0.086475186 0.45886310938747
## 595 sq_drink_alcohol 0.008721390 0.95557605731021
## 596 sq_drink_alcohol 0.053297528 0.71336276014425
## 597 sq_drink_alcohol 0.264186688 0.06864932704253
## 598 sq_average_drink_per_day 0.345986259 0.18167914444427
## 599 sq_average_drink_per_day -0.077065092 0.85474769829995
## 600 sq_average_drink_per_day -0.187162580 0.13320907616193
## 601 sq_self_hep_b -0.043841269 0.70010452722554
## 602 sq_self_hep_b -0.148523564 0.55475236942286
## 603 sq_self_hep_c -0.008504837 0.94062361844989
## 604 sq_self_hep_c -0.133253026 0.58632012495494
## 605 supp_meds_tylenol 0.181507556 0.63535705748588
## 606 supp_meds_tylenol 0.167413591 0.79002340503006
## 607 supp_meds_steroids 0.162567452 0.63085743718607
## 608 supp_meds_steroids -0.402999024 0.70298916766313
## 609 sq_water_well -0.031775990 0.77734871293318
## 610 sq_water_well -0.161526945 0.29269446086610
## 611 sq_water_tap_unfiltered 0.130887263 0.34954163790135
## 612 sq_water_tap_unfiltered 0.265440810 0.04667168825859
## 613 sq_water_house_filtration 0.035397161 0.74609488302110
## 614 sq_water_house_filtration 0.122866958 0.50458013295510
## 615 sq_water_faucet_filter 0.074224982 0.54112145438895
## 616 sq_water_faucet_filter 0.046001885 0.72532178061484
## 617 sq_water_charcoal_filter -0.017015905 0.87904866822130
## 618 sq_water_charcoal_filter -0.007147878 0.96368572588700
## 619 sq_water_bottled -0.303379520 0.02943808106098
## 620 sq_water_bottled -0.527135357 0.00009910794049
## 621 sq_water_none 0.032209065 0.76347440250853
## 622 sq_water_none -0.286379175 0.24456352714546
## 623 sq_water_other_type -0.000698204 0.99487761581831
## 624 sq_water_other_type 0.047675534 0.81640595739470
## 625 source 0.518825224 0.00035296753017
## 626 source 0.273293280 0.02652537691163
## 627 source 1.365624166 0.00000006218287
## 628 sex -0.152564255 0.15690319949755
## 629 race_eth_label -0.099619137 0.69832721067773
## 630 race_eth_label -0.280687329 0.42434022335422
## 631 race_eth_label -0.155025713 0.52926880834616
## 632 race_eth_label -0.240179832 0.43570815119400
## 633 race_final_label 0.363145761 0.60936886944373
## 634 race_final_label -0.125495861 0.80359047025395
## 635 race_final_label 0.085954218 0.88247237807533
## 636 race_final_label -0.557711314 0.17818156233011
## 637 race_final_label 0.057141508 0.62161421926663
## 638 race_final_label 2.154326215 0.03221506802430
## 639 race_final_label -0.132608948 0.65440808798253
## 640 race_final_label 0.322560344 0.40092858744280
## 641 ethnicity -0.143914516 0.55309406174543
## 642 ethnicity -0.240179832 0.43478718661802
## 643 rural -0.030728063 0.86877802201808
## 644 rural 0.227249815 0.16396371383201
## 645 smoking 0.383823460 0.03156290235867
## 646 smoking 0.424432242 0.00024963802757
## 647 sq_drink_alcohol -0.106510076 0.49296709044419
## 648 sq_drink_alcohol 0.296946616 0.03963369285390
## 649 sq_drink_alcohol -0.085498716 0.55172036530078
## 650 sq_average_drink_per_day -0.104427621 0.68793945814010
## 651 sq_average_drink_per_day -0.273190945 0.51845922867124
## 652 sq_average_drink_per_day 0.139826105 0.26388573205593
## 653 sq_self_hep_b 0.338679517 0.00270288931959
## 654 sq_self_hep_b -0.238220118 0.33688740607012
## 655 sq_self_hep_c 0.386303960 0.00064482875449
## 656 sq_self_hep_c 0.339735501 0.15897129077816
## 657 supp_meds_tylenol -0.039791052 0.91717244608162
## 658 supp_meds_tylenol -0.354387040 0.57295049357703
## 659 supp_meds_steroids 0.173481308 0.60813457682163
## 660 supp_meds_steroids 0.657077748 0.53424295732900
## 661 sq_water_well 0.284585889 0.01091883956148
## 662 sq_water_well -0.056920712 0.70803138909272
## 663 sq_water_tap_unfiltered 0.372682966 0.00767897644492
## 664 sq_water_tap_unfiltered 0.132440345 0.31755980881214
## 665 sq_water_house_filtration 0.276526321 0.01100469577270
## 666 sq_water_house_filtration -0.062346803 0.73243980970183
## 667 sq_water_faucet_filter 0.331222398 0.00611511609349
## 668 sq_water_faucet_filter 0.096415171 0.45709065759293
## 669 sq_water_charcoal_filter 0.289475271 0.00910866204876
## 670 sq_water_charcoal_filter -0.085752388 0.58051667684540
## 671 sq_water_bottled 0.329643039 0.01932347445562
## 672 sq_water_bottled 0.069930043 0.60590779765293
## 673 sq_water_none 0.279475921 0.00867219569147
## 674 sq_water_none -0.229269416 0.34669199788674
## 675 sq_water_other_type 0.337214768 0.00169010311525
## 676 sq_water_other_type 0.565255198 0.00524748730282
## 677 source -0.506598992 0.00049247622953
## 678 source -0.713318581 0.00000001380182
## 679 source -0.676877491 0.00655977315887
## 680 sex 0.319078694 0.00293897166878
## 681 race_eth_label -0.384561281 0.13013569457742
## 682 race_eth_label -0.015459945 0.96442794455499
## 683 race_eth_label -0.177342612 0.46598140195592
## 684 race_eth_label 0.282335829 0.35335867587746
## 685 race_final_label 1.135611694 0.10806768285307
## 686 race_final_label -0.425309066 0.39610942244147
## 687 race_final_label -0.471448517 0.41428300467105
## 688 race_final_label 0.306275468 0.45604501729654
## 689 race_final_label -0.307432538 0.00776078864512
## 690 race_final_label -0.471448517 0.63586430273092
## 691 race_final_label -0.420749965 0.15305394925531
## 692 race_final_label -0.167562266 0.66018676901763
## 693 ethnicity -0.232734365 0.33347886020211
## 694 ethnicity 0.282335829 0.35448966539951
## 695 rural -0.017570768 0.92489389560955
## 696 rural -0.109517642 0.50284536393231
## 697 smoking -0.179856476 0.32177091020604
## 698 smoking -0.072533826 0.53558707695853
## 699 sq_drink_alcohol 0.327969771 0.03594188193303
## 700 sq_drink_alcohol 0.024728206 0.86408706078347
## 701 sq_drink_alcohol -0.067189872 0.64121543008338
## 702 sq_average_drink_per_day -0.270412395 0.29799235545930
## 703 sq_average_drink_per_day 0.222094164 0.59912099850931
## 704 sq_average_drink_per_day 0.139006224 0.26608530903584
## 705 sq_self_hep_b -0.080549601 0.47899561405699
## 706 sq_self_hep_b 0.123863430 0.62208712770531
## 707 sq_self_hep_c -0.033324941 0.77046630225326
## 708 sq_self_hep_c -0.013533496 0.95592885498328
## 709 supp_meds_tylenol 0.074785778 0.84501740098413
## 710 supp_meds_tylenol -0.270640942 0.66676216096643
## 711 supp_meds_steroids 0.146786904 0.66450267863285
## 712 supp_meds_steroids -0.055286439 0.95829689341887
## 713 sq_water_well -0.071131278 0.52721094260777
## 714 sq_water_well -0.020271407 0.89496451658351
## 715 sq_water_tap_unfiltered -0.081146477 0.56352426626744
## 716 sq_water_tap_unfiltered -0.119942521 0.36983338632043
## 717 sq_water_house_filtration -0.029969350 0.78307622374691
## 718 sq_water_house_filtration 0.322655831 0.07896650237649
## 719 sq_water_faucet_filter 0.098297135 0.41138610856269
## 720 sq_water_faucet_filter 0.429929406 0.00093424336604
## 721 sq_water_charcoal_filter -0.120332622 0.27988058283521
## 722 sq_water_charcoal_filter -0.289534580 0.06445662344530
## 723 sq_water_bottled -0.103467380 0.46520637575138
## 724 sq_water_bottled -0.107528668 0.43170112368954
## 725 sq_water_none -0.018050259 0.86604968749351
## 726 sq_water_none 0.311629483 0.20541041566927
## 727 sq_water_other_type -0.069122669 0.52440830804744
## 728 sq_water_other_type -0.229725344 0.26277924482141
## Factor PFAS
## 1 sourceDUKE pf_hx_s_scld
## 2 sourceNCSU pf_hx_s_scld
## 3 sourceUNC pf_hx_s_scld
## 4 sexMale pf_hx_s_scld
## 5 race_eth_labelNHB pf_hx_s_scld
## 6 race_eth_labelNHO pf_hx_s_scld
## 7 race_eth_labelNHW pf_hx_s_scld
## 8 race_eth_labelUnknown/Not Reported pf_hx_s_scld
## 9 race_final_labelAmerican Indian pf_hx_s_scld
## 10 race_final_labelAmerican Indian/Alaskan Native pf_hx_s_scld
## 11 race_final_labelAsian pf_hx_s_scld
## 12 race_final_labelAsian/Pacific Islander pf_hx_s_scld
## 13 race_final_labelBlack pf_hx_s_scld
## 14 race_final_labelMore than one race pf_hx_s_scld
## 15 race_final_labelOther pf_hx_s_scld
## 16 race_final_labelUnknown/Not Reported pf_hx_s_scld
## 17 ethnicityNot Hispanic pf_hx_s_scld
## 18 ethnicityUnknown/Not Reported pf_hx_s_scld
## 19 ruralLiving in rural area pf_hx_s_scld
## 20 ruralUnknown/Not Reported pf_hx_s_scld
## 21 smokingSmoke or use vape pf_hx_s_scld
## 22 smokingUnknown/Not Reported pf_hx_s_scld
## 23 sq_drink_alcoholNo, former drinker (stopped) pf_hx_s_scld
## 24 sq_drink_alcoholUnknown/Not Reported pf_hx_s_scld
## 25 sq_drink_alcoholYes, current drinker pf_hx_s_scld
## 26 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_s_scld
## 27 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_s_scld
## 28 sq_average_drink_per_dayUnknown/Not Reported pf_hx_s_scld
## 29 sq_self_hep_bUnknown/Not Reported pf_hx_s_scld
## 30 sq_self_hep_bYes pf_hx_s_scld
## 31 sq_self_hep_cUnknown/Not Reported pf_hx_s_scld
## 32 sq_self_hep_cYes pf_hx_s_scld
## 33 supp_meds_tylenolUnknown/Not Reported pf_hx_s_scld
## 34 supp_meds_tylenolYes pf_hx_s_scld
## 35 supp_meds_steroidsUnknown/Not Reported pf_hx_s_scld
## 36 supp_meds_steroidsYes pf_hx_s_scld
## 37 sq_water_wellUnknown/Not Reported pf_hx_s_scld
## 38 sq_water_wellYes pf_hx_s_scld
## 39 sq_water_tap_unfilteredUnknown/Not Reported pf_hx_s_scld
## 40 sq_water_tap_unfilteredYes pf_hx_s_scld
## 41 sq_water_house_filtrationUnknown/Not Reported pf_hx_s_scld
## 42 sq_water_house_filtrationYes pf_hx_s_scld
## 43 sq_water_faucet_filterUnknown/Not Reported pf_hx_s_scld
## 44 sq_water_faucet_filterYes pf_hx_s_scld
## 45 sq_water_charcoal_filterUnknown/Not Reported pf_hx_s_scld
## 46 sq_water_charcoal_filterYes pf_hx_s_scld
## 47 sq_water_bottledUnknown/Not Reported pf_hx_s_scld
## 48 sq_water_bottledYes pf_hx_s_scld
## 49 sq_water_noneUnknown/Not Reported pf_hx_s_scld
## 50 sq_water_noneYes pf_hx_s_scld
## 51 sq_water_other_typeUnknown/Not Reported pf_hx_s_scld
## 52 sq_water_other_typeYes pf_hx_s_scld
## 53 sourceDUKE pfda_scld
## 54 sourceNCSU pfda_scld
## 55 sourceUNC pfda_scld
## 56 sexMale pfda_scld
## 57 race_eth_labelNHB pfda_scld
## 58 race_eth_labelNHO pfda_scld
## 59 race_eth_labelNHW pfda_scld
## 60 race_eth_labelUnknown/Not Reported pfda_scld
## 61 race_final_labelAmerican Indian pfda_scld
## 62 race_final_labelAmerican Indian/Alaskan Native pfda_scld
## 63 race_final_labelAsian pfda_scld
## 64 race_final_labelAsian/Pacific Islander pfda_scld
## 65 race_final_labelBlack pfda_scld
## 66 race_final_labelMore than one race pfda_scld
## 67 race_final_labelOther pfda_scld
## 68 race_final_labelUnknown/Not Reported pfda_scld
## 69 ethnicityNot Hispanic pfda_scld
## 70 ethnicityUnknown/Not Reported pfda_scld
## 71 ruralLiving in rural area pfda_scld
## 72 ruralUnknown/Not Reported pfda_scld
## 73 smokingSmoke or use vape pfda_scld
## 74 smokingUnknown/Not Reported pfda_scld
## 75 sq_drink_alcoholNo, former drinker (stopped) pfda_scld
## 76 sq_drink_alcoholUnknown/Not Reported pfda_scld
## 77 sq_drink_alcoholYes, current drinker pfda_scld
## 78 sq_average_drink_per_day1-2 alcoholic drinks per day pfda_scld
## 79 sq_average_drink_per_day3-4 alcoholic drinks per day pfda_scld
## 80 sq_average_drink_per_dayUnknown/Not Reported pfda_scld
## 81 sq_self_hep_bUnknown/Not Reported pfda_scld
## 82 sq_self_hep_bYes pfda_scld
## 83 sq_self_hep_cUnknown/Not Reported pfda_scld
## 84 sq_self_hep_cYes pfda_scld
## 85 supp_meds_tylenolUnknown/Not Reported pfda_scld
## 86 supp_meds_tylenolYes pfda_scld
## 87 supp_meds_steroidsUnknown/Not Reported pfda_scld
## 88 supp_meds_steroidsYes pfda_scld
## 89 sq_water_wellUnknown/Not Reported pfda_scld
## 90 sq_water_wellYes pfda_scld
## 91 sq_water_tap_unfilteredUnknown/Not Reported pfda_scld
## 92 sq_water_tap_unfilteredYes pfda_scld
## 93 sq_water_house_filtrationUnknown/Not Reported pfda_scld
## 94 sq_water_house_filtrationYes pfda_scld
## 95 sq_water_faucet_filterUnknown/Not Reported pfda_scld
## 96 sq_water_faucet_filterYes pfda_scld
## 97 sq_water_charcoal_filterUnknown/Not Reported pfda_scld
## 98 sq_water_charcoal_filterYes pfda_scld
## 99 sq_water_bottledUnknown/Not Reported pfda_scld
## 100 sq_water_bottledYes pfda_scld
## 101 sq_water_noneUnknown/Not Reported pfda_scld
## 102 sq_water_noneYes pfda_scld
## 103 sq_water_other_typeUnknown/Not Reported pfda_scld
## 104 sq_water_other_typeYes pfda_scld
## 105 sourceDUKE pfna_scld
## 106 sourceNCSU pfna_scld
## 107 sourceUNC pfna_scld
## 108 sexMale pfna_scld
## 109 race_eth_labelNHB pfna_scld
## 110 race_eth_labelNHO pfna_scld
## 111 race_eth_labelNHW pfna_scld
## 112 race_eth_labelUnknown/Not Reported pfna_scld
## 113 race_final_labelAmerican Indian pfna_scld
## 114 race_final_labelAmerican Indian/Alaskan Native pfna_scld
## 115 race_final_labelAsian pfna_scld
## 116 race_final_labelAsian/Pacific Islander pfna_scld
## 117 race_final_labelBlack pfna_scld
## 118 race_final_labelMore than one race pfna_scld
## 119 race_final_labelOther pfna_scld
## 120 race_final_labelUnknown/Not Reported pfna_scld
## 121 ethnicityNot Hispanic pfna_scld
## 122 ethnicityUnknown/Not Reported pfna_scld
## 123 ruralLiving in rural area pfna_scld
## 124 ruralUnknown/Not Reported pfna_scld
## 125 smokingSmoke or use vape pfna_scld
## 126 smokingUnknown/Not Reported pfna_scld
## 127 sq_drink_alcoholNo, former drinker (stopped) pfna_scld
## 128 sq_drink_alcoholUnknown/Not Reported pfna_scld
## 129 sq_drink_alcoholYes, current drinker pfna_scld
## 130 sq_average_drink_per_day1-2 alcoholic drinks per day pfna_scld
## 131 sq_average_drink_per_day3-4 alcoholic drinks per day pfna_scld
## 132 sq_average_drink_per_dayUnknown/Not Reported pfna_scld
## 133 sq_self_hep_bUnknown/Not Reported pfna_scld
## 134 sq_self_hep_bYes pfna_scld
## 135 sq_self_hep_cUnknown/Not Reported pfna_scld
## 136 sq_self_hep_cYes pfna_scld
## 137 supp_meds_tylenolUnknown/Not Reported pfna_scld
## 138 supp_meds_tylenolYes pfna_scld
## 139 supp_meds_steroidsUnknown/Not Reported pfna_scld
## 140 supp_meds_steroidsYes pfna_scld
## 141 sq_water_wellUnknown/Not Reported pfna_scld
## 142 sq_water_wellYes pfna_scld
## 143 sq_water_tap_unfilteredUnknown/Not Reported pfna_scld
## 144 sq_water_tap_unfilteredYes pfna_scld
## 145 sq_water_house_filtrationUnknown/Not Reported pfna_scld
## 146 sq_water_house_filtrationYes pfna_scld
## 147 sq_water_faucet_filterUnknown/Not Reported pfna_scld
## 148 sq_water_faucet_filterYes pfna_scld
## 149 sq_water_charcoal_filterUnknown/Not Reported pfna_scld
## 150 sq_water_charcoal_filterYes pfna_scld
## 151 sq_water_bottledUnknown/Not Reported pfna_scld
## 152 sq_water_bottledYes pfna_scld
## 153 sq_water_noneUnknown/Not Reported pfna_scld
## 154 sq_water_noneYes pfna_scld
## 155 sq_water_other_typeUnknown/Not Reported pfna_scld
## 156 sq_water_other_typeYes pfna_scld
## 157 sourceDUKE pfos_scld
## 158 sourceNCSU pfos_scld
## 159 sourceUNC pfos_scld
## 160 sexMale pfos_scld
## 161 race_eth_labelNHB pfos_scld
## 162 race_eth_labelNHO pfos_scld
## 163 race_eth_labelNHW pfos_scld
## 164 race_eth_labelUnknown/Not Reported pfos_scld
## 165 race_final_labelAmerican Indian pfos_scld
## 166 race_final_labelAmerican Indian/Alaskan Native pfos_scld
## 167 race_final_labelAsian pfos_scld
## 168 race_final_labelAsian/Pacific Islander pfos_scld
## 169 race_final_labelBlack pfos_scld
## 170 race_final_labelMore than one race pfos_scld
## 171 race_final_labelOther pfos_scld
## 172 race_final_labelUnknown/Not Reported pfos_scld
## 173 ethnicityNot Hispanic pfos_scld
## 174 ethnicityUnknown/Not Reported pfos_scld
## 175 ruralLiving in rural area pfos_scld
## 176 ruralUnknown/Not Reported pfos_scld
## 177 smokingSmoke or use vape pfos_scld
## 178 smokingUnknown/Not Reported pfos_scld
## 179 sq_drink_alcoholNo, former drinker (stopped) pfos_scld
## 180 sq_drink_alcoholUnknown/Not Reported pfos_scld
## 181 sq_drink_alcoholYes, current drinker pfos_scld
## 182 sq_average_drink_per_day1-2 alcoholic drinks per day pfos_scld
## 183 sq_average_drink_per_day3-4 alcoholic drinks per day pfos_scld
## 184 sq_average_drink_per_dayUnknown/Not Reported pfos_scld
## 185 sq_self_hep_bUnknown/Not Reported pfos_scld
## 186 sq_self_hep_bYes pfos_scld
## 187 sq_self_hep_cUnknown/Not Reported pfos_scld
## 188 sq_self_hep_cYes pfos_scld
## 189 supp_meds_tylenolUnknown/Not Reported pfos_scld
## 190 supp_meds_tylenolYes pfos_scld
## 191 supp_meds_steroidsUnknown/Not Reported pfos_scld
## 192 supp_meds_steroidsYes pfos_scld
## 193 sq_water_wellUnknown/Not Reported pfos_scld
## 194 sq_water_wellYes pfos_scld
## 195 sq_water_tap_unfilteredUnknown/Not Reported pfos_scld
## 196 sq_water_tap_unfilteredYes pfos_scld
## 197 sq_water_house_filtrationUnknown/Not Reported pfos_scld
## 198 sq_water_house_filtrationYes pfos_scld
## 199 sq_water_faucet_filterUnknown/Not Reported pfos_scld
## 200 sq_water_faucet_filterYes pfos_scld
## 201 sq_water_charcoal_filterUnknown/Not Reported pfos_scld
## 202 sq_water_charcoal_filterYes pfos_scld
## 203 sq_water_bottledUnknown/Not Reported pfos_scld
## 204 sq_water_bottledYes pfos_scld
## 205 sq_water_noneUnknown/Not Reported pfos_scld
## 206 sq_water_noneYes pfos_scld
## 207 sq_water_other_typeUnknown/Not Reported pfos_scld
## 208 sq_water_other_typeYes pfos_scld
## 209 sourceDUKE pf_hp_a_scld
## 210 sourceNCSU pf_hp_a_scld
## 211 sourceUNC pf_hp_a_scld
## 212 sexMale pf_hp_a_scld
## 213 race_eth_labelNHB pf_hp_a_scld
## 214 race_eth_labelNHO pf_hp_a_scld
## 215 race_eth_labelNHW pf_hp_a_scld
## 216 race_eth_labelUnknown/Not Reported pf_hp_a_scld
## 217 race_final_labelAmerican Indian pf_hp_a_scld
## 218 race_final_labelAmerican Indian/Alaskan Native pf_hp_a_scld
## 219 race_final_labelAsian pf_hp_a_scld
## 220 race_final_labelAsian/Pacific Islander pf_hp_a_scld
## 221 race_final_labelBlack pf_hp_a_scld
## 222 race_final_labelMore than one race pf_hp_a_scld
## 223 race_final_labelOther pf_hp_a_scld
## 224 race_final_labelUnknown/Not Reported pf_hp_a_scld
## 225 ethnicityNot Hispanic pf_hp_a_scld
## 226 ethnicityUnknown/Not Reported pf_hp_a_scld
## 227 ruralLiving in rural area pf_hp_a_scld
## 228 ruralUnknown/Not Reported pf_hp_a_scld
## 229 smokingSmoke or use vape pf_hp_a_scld
## 230 smokingUnknown/Not Reported pf_hp_a_scld
## 231 sq_drink_alcoholNo, former drinker (stopped) pf_hp_a_scld
## 232 sq_drink_alcoholUnknown/Not Reported pf_hp_a_scld
## 233 sq_drink_alcoholYes, current drinker pf_hp_a_scld
## 234 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_a_scld
## 235 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_a_scld
## 236 sq_average_drink_per_dayUnknown/Not Reported pf_hp_a_scld
## 237 sq_self_hep_bUnknown/Not Reported pf_hp_a_scld
## 238 sq_self_hep_bYes pf_hp_a_scld
## 239 sq_self_hep_cUnknown/Not Reported pf_hp_a_scld
## 240 sq_self_hep_cYes pf_hp_a_scld
## 241 supp_meds_tylenolUnknown/Not Reported pf_hp_a_scld
## 242 supp_meds_tylenolYes pf_hp_a_scld
## 243 supp_meds_steroidsUnknown/Not Reported pf_hp_a_scld
## 244 supp_meds_steroidsYes pf_hp_a_scld
## 245 sq_water_wellUnknown/Not Reported pf_hp_a_scld
## 246 sq_water_wellYes pf_hp_a_scld
## 247 sq_water_tap_unfilteredUnknown/Not Reported pf_hp_a_scld
## 248 sq_water_tap_unfilteredYes pf_hp_a_scld
## 249 sq_water_house_filtrationUnknown/Not Reported pf_hp_a_scld
## 250 sq_water_house_filtrationYes pf_hp_a_scld
## 251 sq_water_faucet_filterUnknown/Not Reported pf_hp_a_scld
## 252 sq_water_faucet_filterYes pf_hp_a_scld
## 253 sq_water_charcoal_filterUnknown/Not Reported pf_hp_a_scld
## 254 sq_water_charcoal_filterYes pf_hp_a_scld
## 255 sq_water_bottledUnknown/Not Reported pf_hp_a_scld
## 256 sq_water_bottledYes pf_hp_a_scld
## 257 sq_water_noneUnknown/Not Reported pf_hp_a_scld
## 258 sq_water_noneYes pf_hp_a_scld
## 259 sq_water_other_typeUnknown/Not Reported pf_hp_a_scld
## 260 sq_water_other_typeYes pf_hp_a_scld
## 261 sourceDUKE pfbs_scld
## 262 sourceNCSU pfbs_scld
## 263 sourceUNC pfbs_scld
## 264 sexMale pfbs_scld
## 265 race_eth_labelNHB pfbs_scld
## 266 race_eth_labelNHO pfbs_scld
## 267 race_eth_labelNHW pfbs_scld
## 268 race_eth_labelUnknown/Not Reported pfbs_scld
## 269 race_final_labelAmerican Indian pfbs_scld
## 270 race_final_labelAmerican Indian/Alaskan Native pfbs_scld
## 271 race_final_labelAsian pfbs_scld
## 272 race_final_labelAsian/Pacific Islander pfbs_scld
## 273 race_final_labelBlack pfbs_scld
## 274 race_final_labelMore than one race pfbs_scld
## 275 race_final_labelOther pfbs_scld
## 276 race_final_labelUnknown/Not Reported pfbs_scld
## 277 ethnicityNot Hispanic pfbs_scld
## 278 ethnicityUnknown/Not Reported pfbs_scld
## 279 ruralLiving in rural area pfbs_scld
## 280 ruralUnknown/Not Reported pfbs_scld
## 281 smokingSmoke or use vape pfbs_scld
## 282 smokingUnknown/Not Reported pfbs_scld
## 283 sq_drink_alcoholNo, former drinker (stopped) pfbs_scld
## 284 sq_drink_alcoholUnknown/Not Reported pfbs_scld
## 285 sq_drink_alcoholYes, current drinker pfbs_scld
## 286 sq_average_drink_per_day1-2 alcoholic drinks per day pfbs_scld
## 287 sq_average_drink_per_day3-4 alcoholic drinks per day pfbs_scld
## 288 sq_average_drink_per_dayUnknown/Not Reported pfbs_scld
## 289 sq_self_hep_bUnknown/Not Reported pfbs_scld
## 290 sq_self_hep_bYes pfbs_scld
## 291 sq_self_hep_cUnknown/Not Reported pfbs_scld
## 292 sq_self_hep_cYes pfbs_scld
## 293 supp_meds_tylenolUnknown/Not Reported pfbs_scld
## 294 supp_meds_tylenolYes pfbs_scld
## 295 supp_meds_steroidsUnknown/Not Reported pfbs_scld
## 296 supp_meds_steroidsYes pfbs_scld
## 297 sq_water_wellUnknown/Not Reported pfbs_scld
## 298 sq_water_wellYes pfbs_scld
## 299 sq_water_tap_unfilteredUnknown/Not Reported pfbs_scld
## 300 sq_water_tap_unfilteredYes pfbs_scld
## 301 sq_water_house_filtrationUnknown/Not Reported pfbs_scld
## 302 sq_water_house_filtrationYes pfbs_scld
## 303 sq_water_faucet_filterUnknown/Not Reported pfbs_scld
## 304 sq_water_faucet_filterYes pfbs_scld
## 305 sq_water_charcoal_filterUnknown/Not Reported pfbs_scld
## 306 sq_water_charcoal_filterYes pfbs_scld
## 307 sq_water_bottledUnknown/Not Reported pfbs_scld
## 308 sq_water_bottledYes pfbs_scld
## 309 sq_water_noneUnknown/Not Reported pfbs_scld
## 310 sq_water_noneYes pfbs_scld
## 311 sq_water_other_typeUnknown/Not Reported pfbs_scld
## 312 sq_water_other_typeYes pfbs_scld
## 313 sourceDUKE pfoa_scld
## 314 sourceNCSU pfoa_scld
## 315 sourceUNC pfoa_scld
## 316 sexMale pfoa_scld
## 317 race_eth_labelNHB pfoa_scld
## 318 race_eth_labelNHO pfoa_scld
## 319 race_eth_labelNHW pfoa_scld
## 320 race_eth_labelUnknown/Not Reported pfoa_scld
## 321 race_final_labelAmerican Indian pfoa_scld
## 322 race_final_labelAmerican Indian/Alaskan Native pfoa_scld
## 323 race_final_labelAsian pfoa_scld
## 324 race_final_labelAsian/Pacific Islander pfoa_scld
## 325 race_final_labelBlack pfoa_scld
## 326 race_final_labelMore than one race pfoa_scld
## 327 race_final_labelOther pfoa_scld
## 328 race_final_labelUnknown/Not Reported pfoa_scld
## 329 ethnicityNot Hispanic pfoa_scld
## 330 ethnicityUnknown/Not Reported pfoa_scld
## 331 ruralLiving in rural area pfoa_scld
## 332 ruralUnknown/Not Reported pfoa_scld
## 333 smokingSmoke or use vape pfoa_scld
## 334 smokingUnknown/Not Reported pfoa_scld
## 335 sq_drink_alcoholNo, former drinker (stopped) pfoa_scld
## 336 sq_drink_alcoholUnknown/Not Reported pfoa_scld
## 337 sq_drink_alcoholYes, current drinker pfoa_scld
## 338 sq_average_drink_per_day1-2 alcoholic drinks per day pfoa_scld
## 339 sq_average_drink_per_day3-4 alcoholic drinks per day pfoa_scld
## 340 sq_average_drink_per_dayUnknown/Not Reported pfoa_scld
## 341 sq_self_hep_bUnknown/Not Reported pfoa_scld
## 342 sq_self_hep_bYes pfoa_scld
## 343 sq_self_hep_cUnknown/Not Reported pfoa_scld
## 344 sq_self_hep_cYes pfoa_scld
## 345 supp_meds_tylenolUnknown/Not Reported pfoa_scld
## 346 supp_meds_tylenolYes pfoa_scld
## 347 supp_meds_steroidsUnknown/Not Reported pfoa_scld
## 348 supp_meds_steroidsYes pfoa_scld
## 349 sq_water_wellUnknown/Not Reported pfoa_scld
## 350 sq_water_wellYes pfoa_scld
## 351 sq_water_tap_unfilteredUnknown/Not Reported pfoa_scld
## 352 sq_water_tap_unfilteredYes pfoa_scld
## 353 sq_water_house_filtrationUnknown/Not Reported pfoa_scld
## 354 sq_water_house_filtrationYes pfoa_scld
## 355 sq_water_faucet_filterUnknown/Not Reported pfoa_scld
## 356 sq_water_faucet_filterYes pfoa_scld
## 357 sq_water_charcoal_filterUnknown/Not Reported pfoa_scld
## 358 sq_water_charcoal_filterYes pfoa_scld
## 359 sq_water_bottledUnknown/Not Reported pfoa_scld
## 360 sq_water_bottledYes pfoa_scld
## 361 sq_water_noneUnknown/Not Reported pfoa_scld
## 362 sq_water_noneYes pfoa_scld
## 363 sq_water_other_typeUnknown/Not Reported pfoa_scld
## 364 sq_water_other_typeYes pfoa_scld
## 365 sourceDUKE pf_pe_a_scld
## 366 sourceNCSU pf_pe_a_scld
## 367 sourceUNC pf_pe_a_scld
## 368 sexMale pf_pe_a_scld
## 369 race_eth_labelNHB pf_pe_a_scld
## 370 race_eth_labelNHO pf_pe_a_scld
## 371 race_eth_labelNHW pf_pe_a_scld
## 372 race_eth_labelUnknown/Not Reported pf_pe_a_scld
## 373 race_final_labelAmerican Indian pf_pe_a_scld
## 374 race_final_labelAmerican Indian/Alaskan Native pf_pe_a_scld
## 375 race_final_labelAsian pf_pe_a_scld
## 376 race_final_labelAsian/Pacific Islander pf_pe_a_scld
## 377 race_final_labelBlack pf_pe_a_scld
## 378 race_final_labelMore than one race pf_pe_a_scld
## 379 race_final_labelOther pf_pe_a_scld
## 380 race_final_labelUnknown/Not Reported pf_pe_a_scld
## 381 ethnicityNot Hispanic pf_pe_a_scld
## 382 ethnicityUnknown/Not Reported pf_pe_a_scld
## 383 ruralLiving in rural area pf_pe_a_scld
## 384 ruralUnknown/Not Reported pf_pe_a_scld
## 385 smokingSmoke or use vape pf_pe_a_scld
## 386 smokingUnknown/Not Reported pf_pe_a_scld
## 387 sq_drink_alcoholNo, former drinker (stopped) pf_pe_a_scld
## 388 sq_drink_alcoholUnknown/Not Reported pf_pe_a_scld
## 389 sq_drink_alcoholYes, current drinker pf_pe_a_scld
## 390 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_a_scld
## 391 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_a_scld
## 392 sq_average_drink_per_dayUnknown/Not Reported pf_pe_a_scld
## 393 sq_self_hep_bUnknown/Not Reported pf_pe_a_scld
## 394 sq_self_hep_bYes pf_pe_a_scld
## 395 sq_self_hep_cUnknown/Not Reported pf_pe_a_scld
## 396 sq_self_hep_cYes pf_pe_a_scld
## 397 supp_meds_tylenolUnknown/Not Reported pf_pe_a_scld
## 398 supp_meds_tylenolYes pf_pe_a_scld
## 399 supp_meds_steroidsUnknown/Not Reported pf_pe_a_scld
## 400 supp_meds_steroidsYes pf_pe_a_scld
## 401 sq_water_wellUnknown/Not Reported pf_pe_a_scld
## 402 sq_water_wellYes pf_pe_a_scld
## 403 sq_water_tap_unfilteredUnknown/Not Reported pf_pe_a_scld
## 404 sq_water_tap_unfilteredYes pf_pe_a_scld
## 405 sq_water_house_filtrationUnknown/Not Reported pf_pe_a_scld
## 406 sq_water_house_filtrationYes pf_pe_a_scld
## 407 sq_water_faucet_filterUnknown/Not Reported pf_pe_a_scld
## 408 sq_water_faucet_filterYes pf_pe_a_scld
## 409 sq_water_charcoal_filterUnknown/Not Reported pf_pe_a_scld
## 410 sq_water_charcoal_filterYes pf_pe_a_scld
## 411 sq_water_bottledUnknown/Not Reported pf_pe_a_scld
## 412 sq_water_bottledYes pf_pe_a_scld
## 413 sq_water_noneUnknown/Not Reported pf_pe_a_scld
## 414 sq_water_noneYes pf_pe_a_scld
## 415 sq_water_other_typeUnknown/Not Reported pf_pe_a_scld
## 416 sq_water_other_typeYes pf_pe_a_scld
## 417 sourceDUKE pf_un_a_scld
## 418 sourceNCSU pf_un_a_scld
## 419 sourceUNC pf_un_a_scld
## 420 sexMale pf_un_a_scld
## 421 race_eth_labelNHB pf_un_a_scld
## 422 race_eth_labelNHO pf_un_a_scld
## 423 race_eth_labelNHW pf_un_a_scld
## 424 race_eth_labelUnknown/Not Reported pf_un_a_scld
## 425 race_final_labelAmerican Indian pf_un_a_scld
## 426 race_final_labelAmerican Indian/Alaskan Native pf_un_a_scld
## 427 race_final_labelAsian pf_un_a_scld
## 428 race_final_labelAsian/Pacific Islander pf_un_a_scld
## 429 race_final_labelBlack pf_un_a_scld
## 430 race_final_labelMore than one race pf_un_a_scld
## 431 race_final_labelOther pf_un_a_scld
## 432 race_final_labelUnknown/Not Reported pf_un_a_scld
## 433 ethnicityNot Hispanic pf_un_a_scld
## 434 ethnicityUnknown/Not Reported pf_un_a_scld
## 435 ruralLiving in rural area pf_un_a_scld
## 436 ruralUnknown/Not Reported pf_un_a_scld
## 437 smokingSmoke or use vape pf_un_a_scld
## 438 smokingUnknown/Not Reported pf_un_a_scld
## 439 sq_drink_alcoholNo, former drinker (stopped) pf_un_a_scld
## 440 sq_drink_alcoholUnknown/Not Reported pf_un_a_scld
## 441 sq_drink_alcoholYes, current drinker pf_un_a_scld
## 442 sq_average_drink_per_day1-2 alcoholic drinks per day pf_un_a_scld
## 443 sq_average_drink_per_day3-4 alcoholic drinks per day pf_un_a_scld
## 444 sq_average_drink_per_dayUnknown/Not Reported pf_un_a_scld
## 445 sq_self_hep_bUnknown/Not Reported pf_un_a_scld
## 446 sq_self_hep_bYes pf_un_a_scld
## 447 sq_self_hep_cUnknown/Not Reported pf_un_a_scld
## 448 sq_self_hep_cYes pf_un_a_scld
## 449 supp_meds_tylenolUnknown/Not Reported pf_un_a_scld
## 450 supp_meds_tylenolYes pf_un_a_scld
## 451 supp_meds_steroidsUnknown/Not Reported pf_un_a_scld
## 452 supp_meds_steroidsYes pf_un_a_scld
## 453 sq_water_wellUnknown/Not Reported pf_un_a_scld
## 454 sq_water_wellYes pf_un_a_scld
## 455 sq_water_tap_unfilteredUnknown/Not Reported pf_un_a_scld
## 456 sq_water_tap_unfilteredYes pf_un_a_scld
## 457 sq_water_house_filtrationUnknown/Not Reported pf_un_a_scld
## 458 sq_water_house_filtrationYes pf_un_a_scld
## 459 sq_water_faucet_filterUnknown/Not Reported pf_un_a_scld
## 460 sq_water_faucet_filterYes pf_un_a_scld
## 461 sq_water_charcoal_filterUnknown/Not Reported pf_un_a_scld
## 462 sq_water_charcoal_filterYes pf_un_a_scld
## 463 sq_water_bottledUnknown/Not Reported pf_un_a_scld
## 464 sq_water_bottledYes pf_un_a_scld
## 465 sq_water_noneUnknown/Not Reported pf_un_a_scld
## 466 sq_water_noneYes pf_un_a_scld
## 467 sq_water_other_typeUnknown/Not Reported pf_un_a_scld
## 468 sq_water_other_typeYes pf_un_a_scld
## 469 sourceDUKE pf_hp_s_scld
## 470 sourceNCSU pf_hp_s_scld
## 471 sourceUNC pf_hp_s_scld
## 472 sexMale pf_hp_s_scld
## 473 race_eth_labelNHB pf_hp_s_scld
## 474 race_eth_labelNHO pf_hp_s_scld
## 475 race_eth_labelNHW pf_hp_s_scld
## 476 race_eth_labelUnknown/Not Reported pf_hp_s_scld
## 477 race_final_labelAmerican Indian pf_hp_s_scld
## 478 race_final_labelAmerican Indian/Alaskan Native pf_hp_s_scld
## 479 race_final_labelAsian pf_hp_s_scld
## 480 race_final_labelAsian/Pacific Islander pf_hp_s_scld
## 481 race_final_labelBlack pf_hp_s_scld
## 482 race_final_labelMore than one race pf_hp_s_scld
## 483 race_final_labelOther pf_hp_s_scld
## 484 race_final_labelUnknown/Not Reported pf_hp_s_scld
## 485 ethnicityNot Hispanic pf_hp_s_scld
## 486 ethnicityUnknown/Not Reported pf_hp_s_scld
## 487 ruralLiving in rural area pf_hp_s_scld
## 488 ruralUnknown/Not Reported pf_hp_s_scld
## 489 smokingSmoke or use vape pf_hp_s_scld
## 490 smokingUnknown/Not Reported pf_hp_s_scld
## 491 sq_drink_alcoholNo, former drinker (stopped) pf_hp_s_scld
## 492 sq_drink_alcoholUnknown/Not Reported pf_hp_s_scld
## 493 sq_drink_alcoholYes, current drinker pf_hp_s_scld
## 494 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_s_scld
## 495 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_s_scld
## 496 sq_average_drink_per_dayUnknown/Not Reported pf_hp_s_scld
## 497 sq_self_hep_bUnknown/Not Reported pf_hp_s_scld
## 498 sq_self_hep_bYes pf_hp_s_scld
## 499 sq_self_hep_cUnknown/Not Reported pf_hp_s_scld
## 500 sq_self_hep_cYes pf_hp_s_scld
## 501 supp_meds_tylenolUnknown/Not Reported pf_hp_s_scld
## 502 supp_meds_tylenolYes pf_hp_s_scld
## 503 supp_meds_steroidsUnknown/Not Reported pf_hp_s_scld
## 504 supp_meds_steroidsYes pf_hp_s_scld
## 505 sq_water_wellUnknown/Not Reported pf_hp_s_scld
## 506 sq_water_wellYes pf_hp_s_scld
## 507 sq_water_tap_unfilteredUnknown/Not Reported pf_hp_s_scld
## 508 sq_water_tap_unfilteredYes pf_hp_s_scld
## 509 sq_water_house_filtrationUnknown/Not Reported pf_hp_s_scld
## 510 sq_water_house_filtrationYes pf_hp_s_scld
## 511 sq_water_faucet_filterUnknown/Not Reported pf_hp_s_scld
## 512 sq_water_faucet_filterYes pf_hp_s_scld
## 513 sq_water_charcoal_filterUnknown/Not Reported pf_hp_s_scld
## 514 sq_water_charcoal_filterYes pf_hp_s_scld
## 515 sq_water_bottledUnknown/Not Reported pf_hp_s_scld
## 516 sq_water_bottledYes pf_hp_s_scld
## 517 sq_water_noneUnknown/Not Reported pf_hp_s_scld
## 518 sq_water_noneYes pf_hp_s_scld
## 519 sq_water_other_typeUnknown/Not Reported pf_hp_s_scld
## 520 sq_water_other_typeYes pf_hp_s_scld
## 521 sourceDUKE pf_do_a_scld
## 522 sourceNCSU pf_do_a_scld
## 523 sourceUNC pf_do_a_scld
## 524 sexMale pf_do_a_scld
## 525 race_eth_labelNHB pf_do_a_scld
## 526 race_eth_labelNHO pf_do_a_scld
## 527 race_eth_labelNHW pf_do_a_scld
## 528 race_eth_labelUnknown/Not Reported pf_do_a_scld
## 529 race_final_labelAmerican Indian pf_do_a_scld
## 530 race_final_labelAmerican Indian/Alaskan Native pf_do_a_scld
## 531 race_final_labelAsian pf_do_a_scld
## 532 race_final_labelAsian/Pacific Islander pf_do_a_scld
## 533 race_final_labelBlack pf_do_a_scld
## 534 race_final_labelMore than one race pf_do_a_scld
## 535 race_final_labelOther pf_do_a_scld
## 536 race_final_labelUnknown/Not Reported pf_do_a_scld
## 537 ethnicityNot Hispanic pf_do_a_scld
## 538 ethnicityUnknown/Not Reported pf_do_a_scld
## 539 ruralLiving in rural area pf_do_a_scld
## 540 ruralUnknown/Not Reported pf_do_a_scld
## 541 smokingSmoke or use vape pf_do_a_scld
## 542 smokingUnknown/Not Reported pf_do_a_scld
## 543 sq_drink_alcoholNo, former drinker (stopped) pf_do_a_scld
## 544 sq_drink_alcoholUnknown/Not Reported pf_do_a_scld
## 545 sq_drink_alcoholYes, current drinker pf_do_a_scld
## 546 sq_average_drink_per_day1-2 alcoholic drinks per day pf_do_a_scld
## 547 sq_average_drink_per_day3-4 alcoholic drinks per day pf_do_a_scld
## 548 sq_average_drink_per_dayUnknown/Not Reported pf_do_a_scld
## 549 sq_self_hep_bUnknown/Not Reported pf_do_a_scld
## 550 sq_self_hep_bYes pf_do_a_scld
## 551 sq_self_hep_cUnknown/Not Reported pf_do_a_scld
## 552 sq_self_hep_cYes pf_do_a_scld
## 553 supp_meds_tylenolUnknown/Not Reported pf_do_a_scld
## 554 supp_meds_tylenolYes pf_do_a_scld
## 555 supp_meds_steroidsUnknown/Not Reported pf_do_a_scld
## 556 supp_meds_steroidsYes pf_do_a_scld
## 557 sq_water_wellUnknown/Not Reported pf_do_a_scld
## 558 sq_water_wellYes pf_do_a_scld
## 559 sq_water_tap_unfilteredUnknown/Not Reported pf_do_a_scld
## 560 sq_water_tap_unfilteredYes pf_do_a_scld
## 561 sq_water_house_filtrationUnknown/Not Reported pf_do_a_scld
## 562 sq_water_house_filtrationYes pf_do_a_scld
## 563 sq_water_faucet_filterUnknown/Not Reported pf_do_a_scld
## 564 sq_water_faucet_filterYes pf_do_a_scld
## 565 sq_water_charcoal_filterUnknown/Not Reported pf_do_a_scld
## 566 sq_water_charcoal_filterYes pf_do_a_scld
## 567 sq_water_bottledUnknown/Not Reported pf_do_a_scld
## 568 sq_water_bottledYes pf_do_a_scld
## 569 sq_water_noneUnknown/Not Reported pf_do_a_scld
## 570 sq_water_noneYes pf_do_a_scld
## 571 sq_water_other_typeUnknown/Not Reported pf_do_a_scld
## 572 sq_water_other_typeYes pf_do_a_scld
## 573 sourceDUKE pf_pe_s_scld
## 574 sourceNCSU pf_pe_s_scld
## 575 sourceUNC pf_pe_s_scld
## 576 sexMale pf_pe_s_scld
## 577 race_eth_labelNHB pf_pe_s_scld
## 578 race_eth_labelNHO pf_pe_s_scld
## 579 race_eth_labelNHW pf_pe_s_scld
## 580 race_eth_labelUnknown/Not Reported pf_pe_s_scld
## 581 race_final_labelAmerican Indian pf_pe_s_scld
## 582 race_final_labelAmerican Indian/Alaskan Native pf_pe_s_scld
## 583 race_final_labelAsian pf_pe_s_scld
## 584 race_final_labelAsian/Pacific Islander pf_pe_s_scld
## 585 race_final_labelBlack pf_pe_s_scld
## 586 race_final_labelMore than one race pf_pe_s_scld
## 587 race_final_labelOther pf_pe_s_scld
## 588 race_final_labelUnknown/Not Reported pf_pe_s_scld
## 589 ethnicityNot Hispanic pf_pe_s_scld
## 590 ethnicityUnknown/Not Reported pf_pe_s_scld
## 591 ruralLiving in rural area pf_pe_s_scld
## 592 ruralUnknown/Not Reported pf_pe_s_scld
## 593 smokingSmoke or use vape pf_pe_s_scld
## 594 smokingUnknown/Not Reported pf_pe_s_scld
## 595 sq_drink_alcoholNo, former drinker (stopped) pf_pe_s_scld
## 596 sq_drink_alcoholUnknown/Not Reported pf_pe_s_scld
## 597 sq_drink_alcoholYes, current drinker pf_pe_s_scld
## 598 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_s_scld
## 599 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_s_scld
## 600 sq_average_drink_per_dayUnknown/Not Reported pf_pe_s_scld
## 601 sq_self_hep_bUnknown/Not Reported pf_pe_s_scld
## 602 sq_self_hep_bYes pf_pe_s_scld
## 603 sq_self_hep_cUnknown/Not Reported pf_pe_s_scld
## 604 sq_self_hep_cYes pf_pe_s_scld
## 605 supp_meds_tylenolUnknown/Not Reported pf_pe_s_scld
## 606 supp_meds_tylenolYes pf_pe_s_scld
## 607 supp_meds_steroidsUnknown/Not Reported pf_pe_s_scld
## 608 supp_meds_steroidsYes pf_pe_s_scld
## 609 sq_water_wellUnknown/Not Reported pf_pe_s_scld
## 610 sq_water_wellYes pf_pe_s_scld
## 611 sq_water_tap_unfilteredUnknown/Not Reported pf_pe_s_scld
## 612 sq_water_tap_unfilteredYes pf_pe_s_scld
## 613 sq_water_house_filtrationUnknown/Not Reported pf_pe_s_scld
## 614 sq_water_house_filtrationYes pf_pe_s_scld
## 615 sq_water_faucet_filterUnknown/Not Reported pf_pe_s_scld
## 616 sq_water_faucet_filterYes pf_pe_s_scld
## 617 sq_water_charcoal_filterUnknown/Not Reported pf_pe_s_scld
## 618 sq_water_charcoal_filterYes pf_pe_s_scld
## 619 sq_water_bottledUnknown/Not Reported pf_pe_s_scld
## 620 sq_water_bottledYes pf_pe_s_scld
## 621 sq_water_noneUnknown/Not Reported pf_pe_s_scld
## 622 sq_water_noneYes pf_pe_s_scld
## 623 sq_water_other_typeUnknown/Not Reported pf_pe_s_scld
## 624 sq_water_other_typeYes pf_pe_s_scld
## 625 sourceDUKE pf_hx_a_scld
## 626 sourceNCSU pf_hx_a_scld
## 627 sourceUNC pf_hx_a_scld
## 628 sexMale pf_hx_a_scld
## 629 race_eth_labelNHB pf_hx_a_scld
## 630 race_eth_labelNHO pf_hx_a_scld
## 631 race_eth_labelNHW pf_hx_a_scld
## 632 race_eth_labelUnknown/Not Reported pf_hx_a_scld
## 633 race_final_labelAmerican Indian pf_hx_a_scld
## 634 race_final_labelAmerican Indian/Alaskan Native pf_hx_a_scld
## 635 race_final_labelAsian pf_hx_a_scld
## 636 race_final_labelAsian/Pacific Islander pf_hx_a_scld
## 637 race_final_labelBlack pf_hx_a_scld
## 638 race_final_labelMore than one race pf_hx_a_scld
## 639 race_final_labelOther pf_hx_a_scld
## 640 race_final_labelUnknown/Not Reported pf_hx_a_scld
## 641 ethnicityNot Hispanic pf_hx_a_scld
## 642 ethnicityUnknown/Not Reported pf_hx_a_scld
## 643 ruralLiving in rural area pf_hx_a_scld
## 644 ruralUnknown/Not Reported pf_hx_a_scld
## 645 smokingSmoke or use vape pf_hx_a_scld
## 646 smokingUnknown/Not Reported pf_hx_a_scld
## 647 sq_drink_alcoholNo, former drinker (stopped) pf_hx_a_scld
## 648 sq_drink_alcoholUnknown/Not Reported pf_hx_a_scld
## 649 sq_drink_alcoholYes, current drinker pf_hx_a_scld
## 650 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_a_scld
## 651 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_a_scld
## 652 sq_average_drink_per_dayUnknown/Not Reported pf_hx_a_scld
## 653 sq_self_hep_bUnknown/Not Reported pf_hx_a_scld
## 654 sq_self_hep_bYes pf_hx_a_scld
## 655 sq_self_hep_cUnknown/Not Reported pf_hx_a_scld
## 656 sq_self_hep_cYes pf_hx_a_scld
## 657 supp_meds_tylenolUnknown/Not Reported pf_hx_a_scld
## 658 supp_meds_tylenolYes pf_hx_a_scld
## 659 supp_meds_steroidsUnknown/Not Reported pf_hx_a_scld
## 660 supp_meds_steroidsYes pf_hx_a_scld
## 661 sq_water_wellUnknown/Not Reported pf_hx_a_scld
## 662 sq_water_wellYes pf_hx_a_scld
## 663 sq_water_tap_unfilteredUnknown/Not Reported pf_hx_a_scld
## 664 sq_water_tap_unfilteredYes pf_hx_a_scld
## 665 sq_water_house_filtrationUnknown/Not Reported pf_hx_a_scld
## 666 sq_water_house_filtrationYes pf_hx_a_scld
## 667 sq_water_faucet_filterUnknown/Not Reported pf_hx_a_scld
## 668 sq_water_faucet_filterYes pf_hx_a_scld
## 669 sq_water_charcoal_filterUnknown/Not Reported pf_hx_a_scld
## 670 sq_water_charcoal_filterYes pf_hx_a_scld
## 671 sq_water_bottledUnknown/Not Reported pf_hx_a_scld
## 672 sq_water_bottledYes pf_hx_a_scld
## 673 sq_water_noneUnknown/Not Reported pf_hx_a_scld
## 674 sq_water_noneYes pf_hx_a_scld
## 675 sq_water_other_typeUnknown/Not Reported pf_hx_a_scld
## 676 sq_water_other_typeYes pf_hx_a_scld
## 677 sourceDUKE pfba_scld
## 678 sourceNCSU pfba_scld
## 679 sourceUNC pfba_scld
## 680 sexMale pfba_scld
## 681 race_eth_labelNHB pfba_scld
## 682 race_eth_labelNHO pfba_scld
## 683 race_eth_labelNHW pfba_scld
## 684 race_eth_labelUnknown/Not Reported pfba_scld
## 685 race_final_labelAmerican Indian pfba_scld
## 686 race_final_labelAmerican Indian/Alaskan Native pfba_scld
## 687 race_final_labelAsian pfba_scld
## 688 race_final_labelAsian/Pacific Islander pfba_scld
## 689 race_final_labelBlack pfba_scld
## 690 race_final_labelMore than one race pfba_scld
## 691 race_final_labelOther pfba_scld
## 692 race_final_labelUnknown/Not Reported pfba_scld
## 693 ethnicityNot Hispanic pfba_scld
## 694 ethnicityUnknown/Not Reported pfba_scld
## 695 ruralLiving in rural area pfba_scld
## 696 ruralUnknown/Not Reported pfba_scld
## 697 smokingSmoke or use vape pfba_scld
## 698 smokingUnknown/Not Reported pfba_scld
## 699 sq_drink_alcoholNo, former drinker (stopped) pfba_scld
## 700 sq_drink_alcoholUnknown/Not Reported pfba_scld
## 701 sq_drink_alcoholYes, current drinker pfba_scld
## 702 sq_average_drink_per_day1-2 alcoholic drinks per day pfba_scld
## 703 sq_average_drink_per_day3-4 alcoholic drinks per day pfba_scld
## 704 sq_average_drink_per_dayUnknown/Not Reported pfba_scld
## 705 sq_self_hep_bUnknown/Not Reported pfba_scld
## 706 sq_self_hep_bYes pfba_scld
## 707 sq_self_hep_cUnknown/Not Reported pfba_scld
## 708 sq_self_hep_cYes pfba_scld
## 709 supp_meds_tylenolUnknown/Not Reported pfba_scld
## 710 supp_meds_tylenolYes pfba_scld
## 711 supp_meds_steroidsUnknown/Not Reported pfba_scld
## 712 supp_meds_steroidsYes pfba_scld
## 713 sq_water_wellUnknown/Not Reported pfba_scld
## 714 sq_water_wellYes pfba_scld
## 715 sq_water_tap_unfilteredUnknown/Not Reported pfba_scld
## 716 sq_water_tap_unfilteredYes pfba_scld
## 717 sq_water_house_filtrationUnknown/Not Reported pfba_scld
## 718 sq_water_house_filtrationYes pfba_scld
## 719 sq_water_faucet_filterUnknown/Not Reported pfba_scld
## 720 sq_water_faucet_filterYes pfba_scld
## 721 sq_water_charcoal_filterUnknown/Not Reported pfba_scld
## 722 sq_water_charcoal_filterYes pfba_scld
## 723 sq_water_bottledUnknown/Not Reported pfba_scld
## 724 sq_water_bottledYes pfba_scld
## 725 sq_water_noneUnknown/Not Reported pfba_scld
## 726 sq_water_noneYes pfba_scld
## 727 sq_water_other_typeUnknown/Not Reported pfba_scld
## 728 sq_water_other_typeYes pfba_scld# Combine and print all results
all_results <- rbind(continuous_results, categorical_results)
print(all_results)## Confounders Coeff P
## 1 age_at_enrollment 0.02303963456 0.00002886969906048
## 2 bmi -0.01294812141 0.09611802745084295
## 3 trig_mg_d_l 0.00006026119 0.92894071463283046
## 4 age_at_enrollment 0.01401689114 0.01151280974826796
## 5 bmi -0.01953451965 0.01049324625441954
## 6 trig_mg_d_l -0.00087134772 0.19560135743360868
## 7 age_at_enrollment 0.02285242310 0.00003418692654294
## 8 bmi -0.01016268100 0.19162630103330469
## 9 trig_mg_d_l -0.00012960498 0.84543151201780353
## 10 age_at_enrollment 0.03015972790 0.00000003849922364
## 11 bmi -0.00632410823 0.39797483121906918
## 12 trig_mg_d_l -0.00060423845 0.36564464944825859
## 13 age_at_enrollment 0.00610856935 0.27012846280853531
## 14 bmi -0.00353210464 0.63621285405847949
## 15 trig_mg_d_l -0.00037474559 0.57810255513241993
## 16 age_at_enrollment -0.00039411970 0.94418956366096030
## 17 bmi 0.00294487490 0.67943161804413910
## 18 trig_mg_d_l -0.00051069442 0.45289086346786955
## 19 age_at_enrollment 0.01990379953 0.00032706545799936
## 20 bmi -0.01850220830 0.01730193122393180
## 21 trig_mg_d_l 0.00025406806 0.70465471480120978
## 22 age_at_enrollment 0.00422728729 0.41715088028540981
## 23 bmi -0.00405085209 0.60753830998090086
## 24 trig_mg_d_l -0.00053365608 0.39127771559889879
## 25 age_at_enrollment 0.01474075004 0.00829630670610244
## 26 bmi -0.02788390573 0.00016089041529066
## 27 trig_mg_d_l -0.00105386273 0.11863558257777872
## 28 age_at_enrollment 0.03647003290 0.00000000001254972
## 29 bmi -0.00498536122 0.49783998070122160
## 30 trig_mg_d_l 0.00035106604 0.59487932766741591
## 31 age_at_enrollment 0.00666942172 0.23478386028671305
## 32 bmi -0.02691555479 0.00053014713321087
## 33 trig_mg_d_l -0.00088518107 0.19110820055049224
## 34 age_at_enrollment 0.00723125430 0.18396989965273475
## 35 bmi -0.02551872568 0.00081610677927414
## 36 trig_mg_d_l -0.00011850725 0.86073672780481414
## 37 age_at_enrollment -0.00767217344 0.16771220778328327
## 38 bmi 0.00258183929 0.73804930867047069
## 39 trig_mg_d_l -0.00082944038 0.21370329383697809
## 40 age_at_enrollment 0.00156141004 0.75969057121585326
## 41 bmi -0.00261010632 0.73748118274195495
## 42 trig_mg_d_l -0.00015137036 0.80919075133267548
## 43 source 0.20089579824 0.17816637785405615
## 44 source 0.28773100400 0.02406241741592427
## 45 source -0.24039891626 0.34812012829126060
## 46 sex 0.18719769132 0.08219582251875361
## 47 race_eth_label 0.05078849200 0.84233629638900953
## 48 race_eth_label -0.11510427163 0.74149107535181924
## 49 race_eth_label 0.27422981845 0.26292213105598811
## 50 race_eth_label 0.03888580877 0.89888254812528012
## 51 race_final_label -0.12541197098 0.86037830965615614
## 52 race_final_label -0.56765432777 0.26261635127196636
## 53 race_final_label -0.09411026424 0.87184652297268461
## 54 race_final_label -0.40797332376 0.32604479871633008
## 55 race_final_label -0.20988620230 0.07137795642001048
## 56 race_final_label -0.26436616917 0.79275654669035089
## 57 race_final_label -0.29339427221 0.32387441802348305
## 58 race_final_label -0.14073047032 0.71483775593414878
## 59 ethnicity 0.18917716778 0.43536968718843172
## 60 ethnicity 0.03888580877 0.89927233218110325
## 61 rural -0.24655106670 0.18530426138296391
## 62 rural -0.14787709652 0.36453370981273614
## 63 smoking -0.32381027411 0.07305486987872799
## 64 smoking -0.22948580632 0.04908826819920185
## 65 sq_drink_alcohol -0.09194220808 0.55776878080576808
## 66 sq_drink_alcohol -0.22484955476 0.12238274703819219
## 67 sq_drink_alcohol -0.00556695961 0.96937817849957508
## 68 sq_average_drink_per_day 0.11608471601 0.65578352846413657
## 69 sq_average_drink_per_day -0.26182699346 0.53666952886876229
## 70 sq_average_drink_per_day -0.10529965991 0.40074255201137199
## 71 sq_self_hep_b -0.14193861153 0.21157942812386105
## 72 sq_self_hep_b 0.19215207005 0.44361557003895957
## 73 sq_self_hep_c -0.14660686900 0.19870154554602260
## 74 sq_self_hep_c -0.15523926134 0.52527191567200049
## 75 supp_meds_tylenol 0.30494095510 0.42547659640687141
## 76 supp_meds_tylenol 0.41908332375 0.50488516530129868
## 77 supp_meds_steroids 0.04092410029 0.90371151384786230
## 78 supp_meds_steroids -0.65705384429 0.53425913283768089
## 79 sq_water_well -0.20912105580 0.06225382264052396
## 80 sq_water_well -0.21812448525 0.15388307408751842
## 81 sq_water_tap_unfiltered -0.02362122330 0.86549139364514827
## 82 sq_water_tap_unfiltered 0.24293059625 0.06779584280841820
## 83 sq_water_house_filtration -0.16476832282 0.13133740209378553
## 84 sq_water_house_filtration -0.01594137263 0.93081104646967838
## 85 sq_water_faucet_filter -0.13799983326 0.25477840057299572
## 86 sq_water_faucet_filter 0.05896978212 0.65148799314023809
## 87 sq_water_charcoal_filter -0.15783530672 0.15697464465805316
## 88 sq_water_charcoal_filter 0.07812098769 0.61742210595572367
## 89 sq_water_bottled -0.48924546335 0.00049146516070939
## 90 sq_water_bottled -0.42565285199 0.00165296201584316
## 91 sq_water_none -0.15570725838 0.14497821238538158
## 92 sq_water_none -0.40194594145 0.10160522807343024
## 93 sq_water_other_type -0.14423859690 0.18408036183404880
## 94 sq_water_other_type -0.17196169235 0.40129154196832417
## 95 source -0.24632049084 0.09903735645483616
## 96 source -0.08968999822 0.48062363680237086
## 97 source -0.66412389592 0.00983867718366343
## 98 sex -0.13271702075 0.21831847824619438
## 99 race_eth_label 0.12471352645 0.62644193500230960
## 100 race_eth_label 0.42784565581 0.22209485749044836
## 101 race_eth_label 0.21907404512 0.37257706140278501
## 102 race_eth_label -0.05139885416 0.86705038235471044
## 103 race_final_label -0.12904195608 0.85548381268167228
## 104 race_final_label -0.48022761545 0.34013223492179612
## 105 race_final_label 0.22162573034 0.70227327313975429
## 106 race_final_label 0.73131111208 0.07689498379754887
## 107 race_final_label -0.10601570408 0.35862071309513155
## 108 race_final_label 1.83044970969 0.06776455127016796
## 109 race_final_label -0.18187519687 0.53812055995120500
## 110 race_final_label -0.42628443262 0.26576060685671926
## 111 ethnicity 0.19991584684 0.40915967427517430
## 112 ethnicity -0.05139885416 0.86694944980857691
## 113 rural -0.22321300609 0.23079719614026042
## 114 rural -0.08991929292 0.58161444509189819
## 115 smoking -0.47778649069 0.00796269280740328
## 116 smoking -0.26851843856 0.02067734371999552
## 117 sq_drink_alcohol -0.20787027884 0.17690069057809690
## 118 sq_drink_alcohol -0.13357448819 0.34875449247860013
## 119 sq_drink_alcohol 0.34307142692 0.01623256250436837
## 120 sq_average_drink_per_day -0.06581956258 0.79640617614150799
## 121 sq_average_drink_per_day 0.30971082539 0.45570924404054869
## 122 sq_average_drink_per_day -0.44982168589 0.00027963169345085
## 123 sq_self_hep_b -0.19517872802 0.08568444046719309
## 124 sq_self_hep_b 0.12479265729 0.61823627484439159
## 125 sq_self_hep_c -0.18857557102 0.09718837256862224
## 126 sq_self_hep_c 0.29382990208 0.22767940372032977
## 127 supp_meds_tylenol -0.10442649184 0.78494873278749755
## 128 supp_meds_tylenol 0.13718883533 0.82724883059649901
## 129 supp_meds_steroids -0.20962233873 0.53564564076426890
## 130 supp_meds_steroids -0.22238305274 0.83337724859734486
## 131 sq_water_well -0.13268719802 0.23787633145927936
## 132 sq_water_well -0.09790215802 0.52323933577829118
## 133 sq_water_tap_unfiltered -0.07182516640 0.60904913514095971
## 134 sq_water_tap_unfiltered 0.04709023866 0.72458169476826395
## 135 sq_water_house_filtration -0.11848550442 0.27832139702085046
## 136 sq_water_house_filtration -0.06928087412 0.70639209383938706
## 137 sq_water_faucet_filter -0.11121502339 0.35955630258942806
## 138 sq_water_faucet_filter -0.00361724267 0.97793718366327420
## 139 sq_water_charcoal_filter -0.11828454491 0.28976269305198821
## 140 sq_water_charcoal_filter -0.03045970838 0.84593988600083936
## 141 sq_water_bottled -0.33888418838 0.01623333367882546
## 142 sq_water_bottled -0.32623957681 0.01651137591491339
## 143 sq_water_none -0.08153190201 0.44683501274426518
## 144 sq_water_none -0.16116851633 0.51271635885510181
## 145 sq_water_other_type -0.14035669239 0.19632346742509224
## 146 sq_water_other_type 0.00574184469 0.97764091282821886
## 147 source -0.17536978665 0.24029175304654887
## 148 source -0.00155751589 0.99023454642433784
## 149 source -0.60420136676 0.01887733932902677
## 150 sex -0.18249153129 0.09022350808632779
## 151 race_eth_label 0.11143909821 0.66399374875135109
## 152 race_eth_label 0.07538354085 0.82971361421867285
## 153 race_eth_label 0.23515848590 0.33914499829760592
## 154 race_eth_label 0.01020295708 0.97352278690986982
## 155 race_final_label -0.03100524184 0.96535916817740675
## 156 race_final_label -0.43936740430 0.38636569597660375
## 157 race_final_label -0.01042341464 0.98576358357417404
## 158 race_final_label -0.11707470105 0.77822396547545969
## 159 race_final_label -0.11828239505 0.30947290327306004
## 160 race_final_label 1.41533588799 0.16063322156113585
## 161 race_final_label -0.23230174880 0.43522678347209232
## 162 race_final_label -0.25018833673 0.51661750631772252
## 163 ethnicity 0.19057081425 0.43185377958703053
## 164 ethnicity 0.01020295708 0.97349654239087935
## 165 rural -0.16657258549 0.37123860525779162
## 166 rural -0.12227732775 0.45397511461727769
## 167 smoking -0.55049716636 0.00222769302932113
## 168 smoking -0.24303361665 0.03579987987392344
## 169 sq_drink_alcohol -0.15214353078 0.32289373258876941
## 170 sq_drink_alcohol -0.06457873589 0.65051917779037560
## 171 sq_drink_alcohol 0.39510198704 0.00571563030051612
## 172 sq_average_drink_per_day -0.17936334767 0.48139369719176539
## 173 sq_average_drink_per_day 0.33975607179 0.41243345212405735
## 174 sq_average_drink_per_day -0.47566543041 0.00012072205421371
## 175 sq_self_hep_b -0.16998081239 0.13479409364303724
## 176 sq_self_hep_b 0.07765272744 0.75677422534221872
## 177 sq_self_hep_c -0.15324170559 0.17920855998162405
## 178 sq_self_hep_c -0.04152663629 0.86504317800143804
## 179 supp_meds_tylenol 0.13783174590 0.71761513785548414
## 180 supp_meds_tylenol 1.04553009228 0.09550981098712773
## 181 supp_meds_steroids -0.30899292899 0.36097388049451384
## 182 supp_meds_steroids 0.10145385531 0.92347692411988780
## 183 sq_water_well -0.19791095634 0.07794860503604970
## 184 sq_water_well -0.14505144638 0.34325528210841694
## 185 sq_water_tap_unfiltered -0.06685848275 0.63379349809383023
## 186 sq_water_tap_unfiltered 0.07774682125 0.56054814450977120
## 187 sq_water_house_filtration -0.15719663895 0.15011332667990274
## 188 sq_water_house_filtration -0.06359736630 0.72917960795943682
## 189 sq_water_faucet_filter -0.16765748230 0.16701503357658093
## 190 sq_water_faucet_filter -0.06287281614 0.63038370929637866
## 191 sq_water_charcoal_filter -0.17148290513 0.12453939373143023
## 192 sq_water_charcoal_filter -0.08827373415 0.57281724091754371
## 193 sq_water_bottled -0.31695071374 0.02481255320482565
## 194 sq_water_bottled -0.25741602293 0.05871655083207015
## 195 sq_water_none -0.15039524262 0.15986807660112737
## 196 sq_water_none -0.28059297095 0.25347435646347488
## 197 sq_water_other_type -0.17879316843 0.09958577804326338
## 198 sq_water_other_type -0.06326299693 0.75719660380250520
## 199 source -0.02812398302 0.85071911056610372
## 200 source 0.06545950319 0.60757504689611586
## 201 source -0.54934387889 0.03293039529831022
## 202 sex 0.17018018712 0.11421477651813232
## 203 race_eth_label 0.17735142739 0.48947961492581005
## 204 race_eth_label 0.21368881066 0.54222117666899239
## 205 race_eth_label 0.22072926125 0.36962368798292344
## 206 race_eth_label -0.06425467746 0.83445298522020916
## 207 race_final_label -0.25650160083 0.71719909410729121
## 208 race_final_label -0.61212402402 0.22393436309559747
## 209 race_final_label 0.30221826119 0.60200810793987869
## 210 race_final_label 0.34506062946 0.40285038365018688
## 211 race_final_label -0.03033696946 0.79256603065346942
## 212 race_final_label 2.50633119704 0.01250591825764212
## 213 race_final_label -0.26150115907 0.37590551468124966
## 214 race_final_label -0.35693228717 0.35102751784317132
## 215 ethnicity 0.20729582419 0.39191644876826492
## 216 ethnicity -0.06425467746 0.83404410390462613
## 217 rural -0.15050379014 0.41945767850033333
## 218 rural -0.02567749874 0.87510243365735252
## 219 smoking -0.56816147223 0.00162483462451516
## 220 smoking -0.17764283744 0.12484388703392600
## 221 sq_drink_alcohol -0.09279790313 0.55221901412799523
## 222 sq_drink_alcohol -0.03617257237 0.80250707464714910
## 223 sq_drink_alcohol 0.25098982175 0.08264764890083667
## 224 sq_average_drink_per_day -0.14733130408 0.56878498930871513
## 225 sq_average_drink_per_day 0.13741276657 0.74388410618383627
## 226 sq_average_drink_per_day -0.30905433609 0.01330787645466807
## 227 sq_self_hep_b -0.09608172406 0.39765419685309278
## 228 sq_self_hep_b 0.27321564105 0.27638203097614938
## 229 sq_self_hep_c -0.08827848999 0.43852931773111470
## 230 sq_self_hep_c 0.26631945107 0.27592232776750170
## 231 supp_meds_tylenol 0.22225409868 0.56129273508638611
## 232 supp_meds_tylenol 0.49018745604 0.43550301353660792
## 233 supp_meds_steroids -0.00137046605 0.99676971448586982
## 234 supp_meds_steroids -0.08300121298 0.93744563737665432
## 235 sq_water_well -0.13821435834 0.21869101547986863
## 236 sq_water_well -0.14605474755 0.34083898263584533
## 237 sq_water_tap_unfiltered -0.10884128949 0.43862900238366409
## 238 sq_water_tap_unfiltered -0.03152028087 0.81362118915627635
## 239 sq_water_house_filtration -0.10144195077 0.35332742478950041
## 240 sq_water_house_filtration 0.02881244426 0.87552600125445079
## 241 sq_water_faucet_filter -0.14451134890 0.23371706850434837
## 242 sq_water_faucet_filter -0.11576443844 0.37605746039474852
## 243 sq_water_charcoal_filter -0.09435771005 0.39811618261251014
## 244 sq_water_charcoal_filter 0.07837485262 0.61705027014982561
## 245 sq_water_bottled -0.37438959384 0.00776080774017603
## 246 sq_water_bottled -0.39800413798 0.00339692534565435
## 247 sq_water_none -0.09715229470 0.36444330562043414
## 248 sq_water_none -0.22134405610 0.36842362123646244
## 249 sq_water_other_type -0.11865182176 0.27482381292786012
## 250 sq_water_other_type -0.12858001415 0.53059508137585887
## 251 source 0.10434243584 0.48142224323953187
## 252 source -0.25317070261 0.04569477728082912
## 253 source 0.36671855653 0.15030326977519787
## 254 sex 0.04753972206 0.65952777755572933
## 255 race_eth_label -0.29217269122 0.24558725047749871
## 256 race_eth_label 0.25904064681 0.45098650537993790
## 257 race_eth_label 0.19739330324 0.41292192888074941
## 258 race_eth_label -0.00442983508 0.98826938151728350
## 259 race_final_label 2.99035334157 0.00001177078990472
## 260 race_final_label -0.75076319456 0.11711937574190467
## 261 race_final_label -0.21362555587 0.69833970735643969
## 262 race_final_label -0.20106093275 0.60825038534995124
## 263 race_final_label -0.52810627333 0.00000214067537662
## 264 race_final_label 0.14446620325 0.87920452393557480
## 265 race_final_label -0.54187633507 0.05422224390061022
## 266 race_final_label -0.64172021528 0.07841348875071134
## 267 ethnicity 0.05211948502 0.82999524103981692
## 268 ethnicity -0.00442983508 0.98850968884498225
## 269 rural 0.03981842586 0.83089199323123042
## 270 rural 0.06732367912 0.68047191960674125
## 271 smoking -0.25860857221 0.15406242068696158
## 272 smoking -0.04266261101 0.71521597801003944
## 273 sq_drink_alcohol 0.13586280492 0.38818440115655084
## 274 sq_drink_alcohol 0.04809961613 0.74153340009344393
## 275 sq_drink_alcohol 0.06560293585 0.65217578709575408
## 276 sq_average_drink_per_day 0.34358261099 0.18358567488178576
## 277 sq_average_drink_per_day 1.13305976415 0.00724023699402287
## 278 sq_average_drink_per_day 0.11291955180 0.36326518486072301
## 279 sq_self_hep_b -0.00524517482 0.96324302106208015
## 280 sq_self_hep_b -0.15705182864 0.53229219046349652
## 281 sq_self_hep_c 0.07666664632 0.50144498984545860
## 282 sq_self_hep_c 0.25616463159 0.29506967023341613
## 283 supp_meds_tylenol 0.14236624332 0.70995004114878091
## 284 supp_meds_tylenol 0.09558088307 0.87917492730682845
## 285 supp_meds_steroids 0.10616093523 0.75379520356562613
## 286 supp_meds_steroids -0.00628231156 0.99525978948954863
## 287 sq_water_well -0.07901703786 0.48242764822962969
## 288 sq_water_well -0.02984035320 0.84588876056705242
## 289 sq_water_tap_unfiltered 0.03916307547 0.78055819320376185
## 290 sq_water_tap_unfiltered 0.05959691935 0.65602092727267491
## 291 sq_water_house_filtration -0.06483291389 0.55318487403520389
## 292 sq_water_house_filtration 0.04468035213 0.80820617839498143
## 293 sq_water_faucet_filter 0.04534724923 0.70845488436320547
## 294 sq_water_faucet_filter 0.15306167211 0.24212063939652043
## 295 sq_water_charcoal_filter -0.08703304619 0.43594263519054810
## 296 sq_water_charcoal_filter -0.13294540628 0.39671140672906335
## 297 sq_water_bottled -0.16259904098 0.25016970657882531
## 298 sq_water_bottled -0.21830266548 0.10999177430940106
## 299 sq_water_none -0.05212974655 0.62687470346804519
## 300 sq_water_none 0.04128376170 0.86689577335487455
## 301 sq_water_other_type -0.08433147055 0.43780014503922282
## 302 sq_water_other_type 0.05985138573 0.77047252115801956
## 303 source -0.24209283532 0.10550138874091017
## 304 source -0.31476257326 0.01381394218896274
## 305 source -0.43870902249 0.08785424567301257
## 306 sex -0.07419888249 0.49160923227274667
## 307 race_eth_label -0.01464216199 0.95432754792565500
## 308 race_eth_label -0.02338660368 0.94662591167007082
## 309 race_eth_label 0.21620594043 0.37788131612548015
## 310 race_eth_label 0.29446925111 0.33681006421286097
## 311 race_final_label -0.06497780823 0.92729153707792866
## 312 race_final_label -0.36498230103 0.47055950006093461
## 313 race_final_label -0.28720334611 0.62210940950523153
## 314 race_final_label -0.23482160095 0.57120995522040818
## 315 race_final_label -0.26151753168 0.02465261727798186
## 316 race_final_label -0.32212450954 0.74855511160559651
## 317 race_final_label -0.27609206683 0.35256225658706697
## 318 race_final_label -0.33436959282 0.38492197181345200
## 319 ethnicity 0.13563843592 0.57595190803645446
## 320 ethnicity 0.29446925111 0.33814572280041677
## 321 rural 0.40223577490 0.03046477151052450
## 322 rural -0.02546298067 0.87544973896637868
## 323 smoking 0.02876208111 0.87397920554472974
## 324 smoking -0.13222001617 0.25890118865789558
## 325 sq_drink_alcohol 0.03093608928 0.84399176612941307
## 326 sq_drink_alcohol -0.11354395865 0.43588771007606608
## 327 sq_drink_alcohol 0.04248949045 0.77007949378540197
## 328 sq_average_drink_per_day 0.00173849948 0.99459668702331505
## 329 sq_average_drink_per_day 1.43403530887 0.00065732225553094
## 330 sq_average_drink_per_day 0.00140892991 0.99089640548374591
## 331 sq_self_hep_b -0.16164730427 0.15490547435844459
## 332 sq_self_hep_b -0.23109631067 0.35688641562230339
## 333 sq_self_hep_c -0.11062049213 0.33226578512771388
## 334 sq_self_hep_c 0.11736859792 0.63123268162752577
## 335 supp_meds_tylenol 0.05111422485 0.89376908860933635
## 336 supp_meds_tylenol -0.10000151347 0.87363945925546149
## 337 supp_meds_steroids 0.06764799759 0.84159227516948099
## 338 supp_meds_steroids -0.13545178545 0.89808056927624569
## 339 sq_water_well 0.02866859932 0.79878534040690352
## 340 sq_water_well -0.08368273867 0.58576338919294979
## 341 sq_water_tap_unfiltered -0.04878699378 0.72768354490860843
## 342 sq_water_tap_unfiltered -0.19626578122 0.14160342074544699
## 343 sq_water_house_filtration 0.06616698617 0.54472537125352871
## 344 sq_water_house_filtration 0.18374184953 0.31807308447128430
## 345 sq_water_faucet_filter 0.08488801129 0.48456664213754364
## 346 sq_water_faucet_filter 0.05917333707 0.65126163583327279
## 347 sq_water_charcoal_filter 0.06196009253 0.57934760076694691
## 348 sq_water_charcoal_filter 0.08690500377 0.57974234090501586
## 349 sq_water_bottled 0.00150677405 0.99150577538440487
## 350 sq_water_bottled -0.11319237198 0.40754821004133213
## 351 sq_water_none 0.03361244212 0.75393115719870929
## 352 sq_water_none -0.08365917336 0.73415922696710800
## 353 sq_water_other_type 0.02430377718 0.82315529229054696
## 354 sq_water_other_type -0.04733327578 0.81768277140597645
## 355 source -0.07628426924 0.61053968426051308
## 356 source 0.06932016021 0.58737751970846286
## 357 source -0.42856769023 0.09643431102686301
## 358 sex -0.12516480332 0.24571033496867928
## 359 race_eth_label -0.14944647943 0.55373366719946415
## 360 race_eth_label 0.13817704270 0.68861553399694753
## 361 race_eth_label 0.29883181637 0.21707705491636420
## 362 race_eth_label 0.01456195611 0.96159120427070899
## 363 race_final_label 0.23361134664 0.73943226114684735
## 364 race_final_label -0.68257158805 0.17165331000666206
## 365 race_final_label 0.06701195493 0.90716829723055903
## 366 race_final_label 0.01299508731 0.97464817278574101
## 367 race_final_label -0.43096670466 0.00019045927418025
## 368 race_final_label 0.54236038999 0.58429976766167235
## 369 race_final_label -0.30030274488 0.30534074418752660
## 370 race_final_label 0.02574794177 0.94588993724285442
## 371 ethnicity 0.15605957242 0.51999177754676029
## 372 ethnicity 0.01456195611 0.96220473628700909
## 373 rural -0.19014848628 0.30702073717271200
## 374 rural -0.17063001679 0.29578828788986516
## 375 smoking -0.40978883374 0.02321464375482415
## 376 smoking -0.22263200726 0.05574471466387071
## 377 sq_drink_alcohol -0.11149608945 0.46913030842951009
## 378 sq_drink_alcohol -0.04673299431 0.74326861358173835
## 379 sq_drink_alcohol 0.40955746795 0.00422640049175355
## 380 sq_average_drink_per_day -0.04042465331 0.87408657104043130
## 381 sq_average_drink_per_day -0.26009324830 0.53096833080426087
## 382 sq_average_drink_per_day -0.48147462903 0.00010257804469636
## 383 sq_self_hep_b -0.17885175294 0.11559742974387974
## 384 sq_self_hep_b 0.04522819244 0.85681327359605108
## 385 sq_self_hep_c -0.17786660733 0.11880220685086884
## 386 sq_self_hep_c 0.00515648437 0.98314670068817211
## 387 supp_meds_tylenol 0.44380109830 0.24563690847941486
## 388 supp_meds_tylenol 0.77050797170 0.21985255079337265
## 389 supp_meds_steroids 0.10780508159 0.75003621633080930
## 390 supp_meds_steroids -0.38080944008 0.71869901424882265
## 391 sq_water_well -0.18653694538 0.09675928899760877
## 392 sq_water_well -0.11223939809 0.46350632851723095
## 393 sq_water_tap_unfiltered -0.00717499280 0.95913876245625251
## 394 sq_water_tap_unfiltered 0.17549299403 0.18845245498274266
## 395 sq_water_house_filtration -0.16064803523 0.14111864812130245
## 396 sq_water_house_filtration -0.18414678866 0.31607063806374974
## 397 sq_water_faucet_filter -0.18099109271 0.13559656249832411
## 398 sq_water_faucet_filter -0.14435720063 0.26925087582704033
## 399 sq_water_charcoal_filter -0.17126417939 0.12499948414919104
## 400 sq_water_charcoal_filter -0.04422335533 0.77750970889267790
## 401 sq_water_bottled -0.43728254052 0.00182086074126584
## 402 sq_water_bottled -0.45962196552 0.00069540024129137
## 403 sq_water_none -0.16543176316 0.12150146864343667
## 404 sq_water_none -0.39284391304 0.10950059241961881
## 405 sq_water_other_type -0.18103712113 0.09537905447063832
## 406 sq_water_other_type -0.03766610528 0.85393082686462518
## 407 source -0.59790103757 0.00005228868616012
## 408 source -0.26557025946 0.03370576474755375
## 409 source 0.32661198117 0.19403427471930992
## 410 sex -0.15368685249 0.15386599131093590
## 411 race_eth_label 0.07231545684 0.77714097293777329
## 412 race_eth_label 0.47425865769 0.17483025510465880
## 413 race_eth_label 0.08698417548 0.72246058781372169
## 414 race_eth_label 0.45767860326 0.13554981673753960
## 415 race_final_label 0.34729696079 0.62303943070382883
## 416 race_final_label 1.65297624760 0.00107063992871887
## 417 race_final_label -0.07238566712 0.90034178273734011
## 418 race_final_label -0.04129806505 0.92002234501068081
## 419 race_final_label 0.06298670916 0.58422548881022029
## 420 race_final_label -0.25891127952 0.79509259208137861
## 421 race_final_label -0.20709863531 0.48201712804660735
## 422 race_final_label -0.22782367745 0.55058320024782048
## 423 ethnicity 0.09994099180 0.67927159271312165
## 424 ethnicity 0.45767860326 0.13566211156981897
## 425 rural -0.14723144214 0.42822635251511909
## 426 rural -0.26072761706 0.11000532282995749
## 427 smoking 0.21611201277 0.23338823688726526
## 428 smoking -0.06618776224 0.57137978178831528
## 429 sq_drink_alcohol 0.10156874252 0.51822776011725313
## 430 sq_drink_alcohol -0.00190253764 0.98957692131135044
## 431 sq_drink_alcohol 0.15143861403 0.29776677122813072
## 432 sq_average_drink_per_day 0.05748739112 0.82519940509489453
## 433 sq_average_drink_per_day -0.34492434674 0.41554963544339829
## 434 sq_average_drink_per_day -0.13243173333 0.29051139953655003
## 435 sq_self_hep_b -0.12772680791 0.26138986317014279
## 436 sq_self_hep_b -0.18926614933 0.45099558920554317
## 437 sq_self_hep_c -0.12436403200 0.27562127705553680
## 438 sq_self_hep_c -0.19167028106 0.43308498300754394
## 439 supp_meds_tylenol 0.25950328321 0.49765332661943340
## 440 supp_meds_tylenol 0.07771900517 0.90156876480204828
## 441 supp_meds_steroids 0.21785483823 0.51959775927009866
## 442 supp_meds_steroids -0.29015095263 0.78364104048900063
## 443 sq_water_well -0.03539646609 0.75308976935880034
## 444 sq_water_well -0.00403742287 0.97903005717147185
## 445 sq_water_tap_unfiltered -0.22763959852 0.10460564698569111
## 446 sq_water_tap_unfiltered -0.20965868166 0.11625097875513281
## 447 sq_water_house_filtration 0.03180003008 0.76900375265055310
## 448 sq_water_house_filtration 0.49963711198 0.00639538693209372
## 449 sq_water_faucet_filter -0.00731918832 0.95193128359767076
## 450 sq_water_faucet_filter 0.07300597137 0.57712410414268367
## 451 sq_water_charcoal_filter -0.02645028662 0.81297502605540750
## 452 sq_water_charcoal_filter 0.03855810651 0.80596269263470566
## 453 sq_water_bottled -0.12807664111 0.36598943670439332
## 454 sq_water_bottled -0.10002749761 0.46439951786494849
## 455 sq_water_none -0.00134633678 0.98995994083384609
## 456 sq_water_none -0.33307110634 0.17591982687334426
## 457 sq_water_other_type 0.02991785344 0.78311655900996846
## 458 sq_water_other_type 0.14932828872 0.46695697882131204
## 459 source -0.12233567491 0.40741994614468358
## 460 source 0.16665102186 0.18606871857653695
## 461 source -0.71085759563 0.00529967763418086
## 462 sex -0.08107283790 0.45234435016075591
## 463 race_eth_label 0.38843641214 0.12719466758624104
## 464 race_eth_label 0.75496304658 0.03031047612559701
## 465 race_eth_label 0.30938744827 0.20479634375196532
## 466 race_eth_label -0.06964204857 0.81923914578153800
## 467 race_final_label -0.54217003211 0.43999328372661040
## 468 race_final_label -0.49473597649 0.32117620165951011
## 469 race_final_label 0.31984176703 0.57762843834789201
## 470 race_final_label 1.39032924586 0.00073512345398631
## 471 race_final_label 0.08140436173 0.47660773266544987
## 472 race_final_label 0.53300229345 0.59053852024600917
## 473 race_final_label -0.22594287753 0.44010936421997648
## 474 race_final_label -0.56024015523 0.14005245576397632
## 475 ethnicity 0.35330235643 0.14289994452075006
## 476 ethnicity -0.06964204857 0.81947623415181903
## 477 rural -0.21079073860 0.25793964453798679
## 478 rural -0.04719442936 0.77247965322541445
## 479 smoking -0.55938000641 0.00181874007453686
## 480 smoking -0.32017764170 0.00560773476027302
## 481 sq_drink_alcohol -0.17786232689 0.24777442714207512
## 482 sq_drink_alcohol -0.16663937622 0.24258750496615397
## 483 sq_drink_alcohol 0.34004163394 0.01719459843915620
## 484 sq_average_drink_per_day 0.16709286937 0.51266621906039722
## 485 sq_average_drink_per_day 0.03513635302 0.93254748917459640
## 486 sq_average_drink_per_day -0.42766182037 0.00054676111079222
## 487 sq_self_hep_b -0.22785512960 0.04436660508739628
## 488 sq_self_hep_b 0.24387552625 0.32895313970867956
## 489 sq_self_hep_c -0.23110609461 0.04207291408647388
## 490 sq_self_hep_c 0.22064001249 0.36434293822168251
## 491 supp_meds_tylenol -0.32443230158 0.39565811530909123
## 492 supp_meds_tylenol 0.26527944634 0.67233383417726300
## 493 supp_meds_steroids -0.43690149238 0.19622409137501412
## 494 supp_meds_steroids -0.02186261809 0.98346705424688374
## 495 sq_water_well -0.12817120720 0.25427262322351152
## 496 sq_water_well -0.02495098293 0.87074988658916408
## 497 sq_water_tap_unfiltered -0.22205510583 0.11348990546404761
## 498 sq_water_tap_unfiltered -0.07001753613 0.59948647097832886
## 499 sq_water_house_filtration -0.13263834075 0.22481218952720550
## 500 sq_water_house_filtration -0.03978205473 0.82866854646978583
## 501 sq_water_faucet_filter -0.13144880015 0.27842603953196615
## 502 sq_water_faucet_filter 0.02976012029 0.81982321014451420
## 503 sq_water_charcoal_filter -0.11479817257 0.30392858519815302
## 504 sq_water_charcoal_filter 0.05286413942 0.73582611255727159
## 505 sq_water_bottled -0.35114193595 0.01283108050418380
## 506 sq_water_bottled -0.28139780657 0.03857335714816601
## 507 sq_water_none -0.09645572879 0.36794161779746204
## 508 sq_water_none -0.20912526088 0.39547290349843378
## 509 sq_water_other_type -0.13920849554 0.20007125981871401
## 510 sq_water_other_type -0.07811881114 0.70304722582391288
## 511 source 0.06168004872 0.67844612713381902
## 512 source 0.22832573243 0.07252736028874485
## 513 source -0.45648133987 0.07474208092999160
## 514 sex 0.30350051034 0.00469493718683770
## 515 race_eth_label 0.17144822696 0.50197497862570550
## 516 race_eth_label -0.08286614721 0.81223970154000491
## 517 race_eth_label 0.35914307931 0.14277455985685836
## 518 race_eth_label 0.17094081549 0.57643705503039200
## 519 race_final_label -0.35595703100 0.61712705883957342
## 520 race_final_label -0.75988071120 0.13339352193114282
## 521 race_final_label -0.06325872262 0.91352122741302155
## 522 race_final_label -0.37547025156 0.36528543014309145
## 523 race_final_label -0.16785923828 0.14835335109303602
## 524 race_final_label 1.00801708607 0.31601277293424263
## 525 race_final_label -0.25448828409 0.39135877967638943
## 526 race_final_label -0.27232894289 0.47898862867843928
## 527 ethnicity 0.28253434133 0.24394937568137418
## 528 ethnicity 0.17094081549 0.57772753504935903
## 529 rural -0.15330859096 0.41080517756177970
## 530 rural -0.05287271038 0.74619014229260427
## 531 smoking -0.59743152776 0.00092276549007707
## 532 smoking -0.12179225914 0.29205421769068646
## 533 sq_drink_alcohol -0.06535445769 0.67660542380994215
## 534 sq_drink_alcohol 0.01175053534 0.93549031572167918
## 535 sq_drink_alcohol 0.20806866087 0.15152091369328727
## 536 sq_average_drink_per_day 0.01595902681 0.95098333148843639
## 537 sq_average_drink_per_day 0.06376641718 0.88000354530749192
## 538 sq_average_drink_per_day -0.21608972737 0.08417452030592833
## 539 sq_self_hep_b -0.02247278890 0.84347580470059136
## 540 sq_self_hep_b 0.14660302112 0.55989141502025297
## 541 sq_self_hep_c -0.02853443225 0.80272136125276106
## 542 sq_self_hep_c 0.02671288685 0.91313950657492171
## 543 supp_meds_tylenol 0.30269349191 0.42836384684072104
## 544 supp_meds_tylenol 0.78143889628 0.21353816457676289
## 545 supp_meds_steroids -0.10559143425 0.75503614022516952
## 546 supp_meds_steroids -0.46625437338 0.65925938965952646
## 547 sq_water_well -0.06777253946 0.54651262733604811
## 548 sq_water_well -0.15540013737 0.31136152736212841
## 549 sq_water_tap_unfiltered -0.00727570282 0.95872659269934801
## 550 sq_water_tap_unfiltered 0.02603227415 0.84574417416999270
## 551 sq_water_house_filtration -0.03605705995 0.74164992928474538
## 552 sq_water_house_filtration -0.01642026493 0.92895252444544685
## 553 sq_water_faucet_filter -0.07838889488 0.51835547757238953
## 554 sq_water_faucet_filter -0.12496215620 0.33973975540600310
## 555 sq_water_charcoal_filter -0.04281037697 0.70176869616976578
## 556 sq_water_charcoal_filter 0.02425610311 0.87718241871528879
## 557 sq_water_bottled -0.34343405338 0.01441692733043675
## 558 sq_water_bottled -0.43313180106 0.00143238137611300
## 559 sq_water_none -0.03958620288 0.71147423698221934
## 560 sq_water_none -0.32045363493 0.19292650115117382
## 561 sq_water_other_type -0.04449232868 0.68223041086397185
## 562 sq_water_other_type -0.16656898382 0.41705478475211033
## 563 source 0.00341597575 0.98179146397574657
## 564 source 0.04502069166 0.72434393181777690
## 565 source -0.51200370343 0.04708117779278184
## 566 sex -0.02359390772 0.82693773382376023
## 567 race_eth_label 0.06086368697 0.81187103175202535
## 568 race_eth_label 0.28356857907 0.41725220517750161
## 569 race_eth_label 0.17080174030 0.48604179615837562
## 570 race_eth_label -0.23994111834 0.43384263785675281
## 571 race_final_label -0.46259942334 0.51535911876491247
## 572 race_final_label -0.48699606048 0.33501616397590472
## 573 race_final_label 0.00662795737 0.99090900750855360
## 574 race_final_label 0.76233095269 0.06613992103282181
## 575 race_final_label -0.10757306829 0.35324429414612923
## 576 race_final_label -0.15142758139 0.88002788778056229
## 577 race_final_label -0.07631609842 0.79678214569844263
## 578 race_final_label -0.54040796213 0.15992207734304267
## 579 ethnicity 0.14262137861 0.55500838863838109
## 580 ethnicity -0.23994111834 0.43331808561261076
## 581 rural -0.13286357000 0.47604978557855959
## 582 rural 0.00406386978 0.98015694799679176
## 583 smoking -0.33925333501 0.06089981157603477
## 584 smoking -0.16043150025 0.16912075431887635
## 585 sq_drink_alcohol 0.03369684146 0.82844187593956276
## 586 sq_drink_alcohol 0.03928903694 0.78511449567257685
## 587 sq_drink_alcohol 0.37799051426 0.00887878150753327
## 588 sq_average_drink_per_day -0.14649866148 0.56947537328377917
## 589 sq_average_drink_per_day -0.00615415023 0.98827941703331035
## 590 sq_average_drink_per_day -0.37744411216 0.00245565678435985
## 591 sq_self_hep_b -0.14105158765 0.21493465638586645
## 592 sq_self_hep_b 0.04210692449 0.86677177612829825
## 593 sq_self_hep_c -0.09979353088 0.37976264387495307
## 594 sq_self_hep_c 0.42840676793 0.07911871955041586
## 595 supp_meds_tylenol -0.24174331541 0.52552310304422201
## 596 supp_meds_tylenol 0.74300215321 0.23522327501678239
## 597 supp_meds_steroids -0.21852365905 0.51797858027497301
## 598 supp_meds_steroids 0.71454274816 0.49868186690490035
## 599 sq_water_well 0.01168605994 0.91721458430984049
## 600 sq_water_well 0.12608602047 0.41156166219780010
## 601 sq_water_tap_unfiltered -0.09220049374 0.51184853848187739
## 602 sq_water_tap_unfiltered -0.07543539858 0.57279182355582148
## 603 sq_water_house_filtration -0.01192867507 0.91310090818639877
## 604 sq_water_house_filtration -0.12977075568 0.48093371950682218
## 605 sq_water_faucet_filter -0.02728505772 0.82219076468470564
## 606 sq_water_faucet_filter -0.09022857915 0.49076539977288947
## 607 sq_water_charcoal_filter 0.03682718974 0.74186846476577573
## 608 sq_water_charcoal_filter 0.04040313633 0.79688719547918652
## 609 sq_water_bottled -0.17773609857 0.20792533592251639
## 610 sq_water_bottled -0.27538320757 0.04357849187013037
## 611 sq_water_none 0.02229770786 0.83524455521134733
## 612 sq_water_none -0.12918736057 0.59997849432490002
## 613 sq_water_other_type -0.06032328779 0.57889504928244273
## 614 sq_water_other_type 0.09236555500 0.65262891764171804
## 615 source 0.12846146544 0.38687468700134564
## 616 source 0.39720909646 0.00181891699048696
## 617 source 0.04155554741 0.87053458291733876
## 618 sex 0.08119651991 0.45165441374253601
## 619 race_eth_label -0.00586181941 0.98166500714231375
## 620 race_eth_label 0.07238360316 0.83548591495916558
## 621 race_eth_label 0.27902406267 0.25418843077719411
## 622 race_eth_label 0.04716044722 0.87739425723636211
## 623 race_final_label 1.36028732641 0.05400990074290221
## 624 race_final_label -0.67792247840 0.17574960546629939
## 625 race_final_label -0.05066729394 0.92993187406726230
## 626 race_final_label -0.43348449368 0.29078243295853851
## 627 race_final_label -0.30480826458 0.00818424840147474
## 628 race_final_label 0.21183589124 0.83120895797991856
## 629 race_final_label -0.36639427441 0.21245914434554627
## 630 race_final_label -0.37159837289 0.32895717410028713
## 631 ethnicity 0.18359567650 0.44910052978292048
## 632 ethnicity 0.04716044722 0.87801175679233379
## 633 rural -0.29369835283 0.11397432465805926
## 634 rural -0.22595188987 0.16528201759409997
## 635 smoking -0.32812133861 0.07025007004597797
## 636 smoking -0.08647518574 0.45886310938747255
## 637 sq_drink_alcohol 0.00872138962 0.95557605731020900
## 638 sq_drink_alcohol 0.05329752766 0.71336276014425226
## 639 sq_drink_alcohol 0.26418668784 0.06864932704252635
## 640 sq_average_drink_per_day 0.34598625878 0.18167914444426966
## 641 sq_average_drink_per_day -0.07706509184 0.85474769829995156
## 642 sq_average_drink_per_day -0.18716258000 0.13320907616193359
## 643 sq_self_hep_b -0.04384126872 0.70010452722554173
## 644 sq_self_hep_b -0.14852356441 0.55475236942286110
## 645 sq_self_hep_c -0.00850483650 0.94062361844988573
## 646 sq_self_hep_c -0.13325302642 0.58632012495494368
## 647 supp_meds_tylenol 0.18150755550 0.63535705748587723
## 648 supp_meds_tylenol 0.16741359119 0.79002340503005719
## 649 supp_meds_steroids 0.16256745203 0.63085743718607123
## 650 supp_meds_steroids -0.40299902397 0.70298916766313346
## 651 sq_water_well -0.03177598964 0.77734871293318042
## 652 sq_water_well -0.16152694531 0.29269446086609541
## 653 sq_water_tap_unfiltered 0.13088726281 0.34954163790135262
## 654 sq_water_tap_unfiltered 0.26544081020 0.04667168825859383
## 655 sq_water_house_filtration 0.03539716070 0.74609488302110494
## 656 sq_water_house_filtration 0.12286695845 0.50458013295510473
## 657 sq_water_faucet_filter 0.07422498204 0.54112145438894954
## 658 sq_water_faucet_filter 0.04600188469 0.72532178061484442
## 659 sq_water_charcoal_filter -0.01701590491 0.87904866822130390
## 660 sq_water_charcoal_filter -0.00714787774 0.96368572588700163
## 661 sq_water_bottled -0.30337952011 0.02943808106098152
## 662 sq_water_bottled -0.52713535708 0.00009910794049467
## 663 sq_water_none 0.03220906487 0.76347440250852572
## 664 sq_water_none -0.28637917545 0.24456352714546090
## 665 sq_water_other_type -0.00069820403 0.99487761581831213
## 666 sq_water_other_type 0.04767553387 0.81640595739469646
## 667 source 0.51882522422 0.00035296753016782
## 668 source 0.27329328029 0.02652537691163277
## 669 source 1.36562416595 0.00000006218287011
## 670 sex -0.15256425529 0.15690319949754505
## 671 race_eth_label -0.09961913738 0.69832721067772541
## 672 race_eth_label -0.28068732935 0.42434022335421961
## 673 race_eth_label -0.15502571296 0.52926880834615642
## 674 race_eth_label -0.24017983164 0.43570815119399686
## 675 race_final_label 0.36314576107 0.60936886944372626
## 676 race_final_label -0.12549586113 0.80359047025394681
## 677 race_final_label 0.08595421817 0.88247237807532541
## 678 race_final_label -0.55771131397 0.17818156233011151
## 679 race_final_label 0.05714150773 0.62161421926663007
## 680 race_final_label 2.15432621489 0.03221506802430414
## 681 race_final_label -0.13260894822 0.65440808798253147
## 682 race_final_label 0.32256034411 0.40092858744280124
## 683 ethnicity -0.14391451569 0.55309406174542874
## 684 ethnicity -0.24017983164 0.43478718661802029
## 685 rural -0.03072806294 0.86877802201807885
## 686 rural 0.22724981485 0.16396371383201136
## 687 smoking 0.38382346020 0.03156290235866580
## 688 smoking 0.42443224230 0.00024963802756887
## 689 sq_drink_alcohol -0.10651007576 0.49296709044419185
## 690 sq_drink_alcohol 0.29694661582 0.03963369285390450
## 691 sq_drink_alcohol -0.08549871620 0.55172036530077662
## 692 sq_average_drink_per_day -0.10442762134 0.68793945814009794
## 693 sq_average_drink_per_day -0.27319094465 0.51845922867124128
## 694 sq_average_drink_per_day 0.13982610455 0.26388573205592636
## 695 sq_self_hep_b 0.33867951731 0.00270288931958967
## 696 sq_self_hep_b -0.23822011787 0.33688740607011625
## 697 sq_self_hep_c 0.38630395960 0.00064482875448686
## 698 sq_self_hep_c 0.33973550067 0.15897129077815536
## 699 supp_meds_tylenol -0.03979105231 0.91717244608161719
## 700 supp_meds_tylenol -0.35438704003 0.57295049357703465
## 701 supp_meds_steroids 0.17348130782 0.60813457682162686
## 702 supp_meds_steroids 0.65707774780 0.53424295732899529
## 703 sq_water_well 0.28458588928 0.01091883956148439
## 704 sq_water_well -0.05692071246 0.70803138909271612
## 705 sq_water_tap_unfiltered 0.37268296635 0.00767897644492330
## 706 sq_water_tap_unfiltered 0.13244034482 0.31755980881213985
## 707 sq_water_house_filtration 0.27652632056 0.01100469577269840
## 708 sq_water_house_filtration -0.06234680346 0.73243980970183031
## 709 sq_water_faucet_filter 0.33122239842 0.00611511609349293
## 710 sq_water_faucet_filter 0.09641517113 0.45709065759293344
## 711 sq_water_charcoal_filter 0.28947527111 0.00910866204875724
## 712 sq_water_charcoal_filter -0.08575238784 0.58051667684539865
## 713 sq_water_bottled 0.32964303863 0.01932347445562221
## 714 sq_water_bottled 0.06993004302 0.60590779765292924
## 715 sq_water_none 0.27947592083 0.00867219569146533
## 716 sq_water_none -0.22926941595 0.34669199788674454
## 717 sq_water_other_type 0.33721476837 0.00169010311525126
## 718 sq_water_other_type 0.56525519790 0.00524748730281680
## 719 source -0.50659899172 0.00049247622953313
## 720 source -0.71331858138 0.00000001380182137
## 721 source -0.67687749138 0.00655977315886617
## 722 sex 0.31907869439 0.00293897166877725
## 723 race_eth_label -0.38456128103 0.13013569457742086
## 724 race_eth_label -0.01545994530 0.96442794455498793
## 725 race_eth_label -0.17734261188 0.46598140195591820
## 726 race_eth_label 0.28233582862 0.35335867587746195
## 727 race_final_label 1.13561169417 0.10806768285307439
## 728 race_final_label -0.42530906633 0.39610942244147207
## 729 race_final_label -0.47144851746 0.41428300467104739
## 730 race_final_label 0.30627546779 0.45604501729654012
## 731 race_final_label -0.30743253810 0.00776078864512350
## 732 race_final_label -0.47144851746 0.63586430273091588
## 733 race_final_label -0.42074996522 0.15305394925530741
## 734 race_final_label -0.16756226591 0.66018676901762929
## 735 ethnicity -0.23273436513 0.33347886020210904
## 736 ethnicity 0.28233582862 0.35448966539951243
## 737 rural -0.01757076775 0.92489389560955348
## 738 rural -0.10951764199 0.50284536393231494
## 739 smoking -0.17985647561 0.32177091020604093
## 740 smoking -0.07253382596 0.53558707695853414
## 741 sq_drink_alcohol 0.32796977117 0.03594188193302610
## 742 sq_drink_alcohol 0.02472820551 0.86408706078347330
## 743 sq_drink_alcohol -0.06718987230 0.64121543008337678
## 744 sq_average_drink_per_day -0.27041239489 0.29799235545930247
## 745 sq_average_drink_per_day 0.22209416367 0.59912099850931455
## 746 sq_average_drink_per_day 0.13900622351 0.26608530903584476
## 747 sq_self_hep_b -0.08054960079 0.47899561405698610
## 748 sq_self_hep_b 0.12386342952 0.62208712770531249
## 749 sq_self_hep_c -0.03332494094 0.77046630225326396
## 750 sq_self_hep_c -0.01353349626 0.95592885498327729
## 751 supp_meds_tylenol 0.07478577777 0.84501740098412648
## 752 supp_meds_tylenol -0.27064094184 0.66676216096642582
## 753 supp_meds_steroids 0.14678690410 0.66450267863284707
## 754 supp_meds_steroids -0.05528643906 0.95829689341887170
## 755 sq_water_well -0.07113127787 0.52721094260777346
## 756 sq_water_well -0.02027140684 0.89496451658350551
## 757 sq_water_tap_unfiltered -0.08114647657 0.56352426626744490
## 758 sq_water_tap_unfiltered -0.11994252123 0.36983338632042684
## 759 sq_water_house_filtration -0.02996935033 0.78307622374691188
## 760 sq_water_house_filtration 0.32265583066 0.07896650237649259
## 761 sq_water_faucet_filter 0.09829713530 0.41138610856268598
## 762 sq_water_faucet_filter 0.42992940577 0.00093424336603878
## 763 sq_water_charcoal_filter -0.12033262226 0.27988058283521206
## 764 sq_water_charcoal_filter -0.28953458010 0.06445662344530483
## 765 sq_water_bottled -0.10346737984 0.46520637575138080
## 766 sq_water_bottled -0.10752866806 0.43170112368953550
## 767 sq_water_none -0.01805025876 0.86604968749351308
## 768 sq_water_none 0.31162948316 0.20541041566927390
## 769 sq_water_other_type -0.06912266856 0.52440830804744287
## 770 sq_water_other_type -0.22972534384 0.26277924482141085
## Factor PFAS
## 1 age_at_enrollment pf_hx_s_scld
## 2 bmi pf_hx_s_scld
## 3 trig_mg_d_l pf_hx_s_scld
## 4 age_at_enrollment pfda_scld
## 5 bmi pfda_scld
## 6 trig_mg_d_l pfda_scld
## 7 age_at_enrollment pfna_scld
## 8 bmi pfna_scld
## 9 trig_mg_d_l pfna_scld
## 10 age_at_enrollment pfos_scld
## 11 bmi pfos_scld
## 12 trig_mg_d_l pfos_scld
## 13 age_at_enrollment pf_hp_a_scld
## 14 bmi pf_hp_a_scld
## 15 trig_mg_d_l pf_hp_a_scld
## 16 age_at_enrollment pfbs_scld
## 17 bmi pfbs_scld
## 18 trig_mg_d_l pfbs_scld
## 19 age_at_enrollment pfoa_scld
## 20 bmi pfoa_scld
## 21 trig_mg_d_l pfoa_scld
## 22 age_at_enrollment pf_pe_a_scld
## 23 bmi pf_pe_a_scld
## 24 trig_mg_d_l pf_pe_a_scld
## 25 age_at_enrollment pf_un_a_scld
## 26 bmi pf_un_a_scld
## 27 trig_mg_d_l pf_un_a_scld
## 28 age_at_enrollment pf_hp_s_scld
## 29 bmi pf_hp_s_scld
## 30 trig_mg_d_l pf_hp_s_scld
## 31 age_at_enrollment pf_do_a_scld
## 32 bmi pf_do_a_scld
## 33 trig_mg_d_l pf_do_a_scld
## 34 age_at_enrollment pf_pe_s_scld
## 35 bmi pf_pe_s_scld
## 36 trig_mg_d_l pf_pe_s_scld
## 37 age_at_enrollment pf_hx_a_scld
## 38 bmi pf_hx_a_scld
## 39 trig_mg_d_l pf_hx_a_scld
## 40 age_at_enrollment pfba_scld
## 41 bmi pfba_scld
## 42 trig_mg_d_l pfba_scld
## 43 sourceDUKE pf_hx_s_scld
## 44 sourceNCSU pf_hx_s_scld
## 45 sourceUNC pf_hx_s_scld
## 46 sexMale pf_hx_s_scld
## 47 race_eth_labelNHB pf_hx_s_scld
## 48 race_eth_labelNHO pf_hx_s_scld
## 49 race_eth_labelNHW pf_hx_s_scld
## 50 race_eth_labelUnknown/Not Reported pf_hx_s_scld
## 51 race_final_labelAmerican Indian pf_hx_s_scld
## 52 race_final_labelAmerican Indian/Alaskan Native pf_hx_s_scld
## 53 race_final_labelAsian pf_hx_s_scld
## 54 race_final_labelAsian/Pacific Islander pf_hx_s_scld
## 55 race_final_labelBlack pf_hx_s_scld
## 56 race_final_labelMore than one race pf_hx_s_scld
## 57 race_final_labelOther pf_hx_s_scld
## 58 race_final_labelUnknown/Not Reported pf_hx_s_scld
## 59 ethnicityNot Hispanic pf_hx_s_scld
## 60 ethnicityUnknown/Not Reported pf_hx_s_scld
## 61 ruralLiving in rural area pf_hx_s_scld
## 62 ruralUnknown/Not Reported pf_hx_s_scld
## 63 smokingSmoke or use vape pf_hx_s_scld
## 64 smokingUnknown/Not Reported pf_hx_s_scld
## 65 sq_drink_alcoholNo, former drinker (stopped) pf_hx_s_scld
## 66 sq_drink_alcoholUnknown/Not Reported pf_hx_s_scld
## 67 sq_drink_alcoholYes, current drinker pf_hx_s_scld
## 68 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_s_scld
## 69 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_s_scld
## 70 sq_average_drink_per_dayUnknown/Not Reported pf_hx_s_scld
## 71 sq_self_hep_bUnknown/Not Reported pf_hx_s_scld
## 72 sq_self_hep_bYes pf_hx_s_scld
## 73 sq_self_hep_cUnknown/Not Reported pf_hx_s_scld
## 74 sq_self_hep_cYes pf_hx_s_scld
## 75 supp_meds_tylenolUnknown/Not Reported pf_hx_s_scld
## 76 supp_meds_tylenolYes pf_hx_s_scld
## 77 supp_meds_steroidsUnknown/Not Reported pf_hx_s_scld
## 78 supp_meds_steroidsYes pf_hx_s_scld
## 79 sq_water_wellUnknown/Not Reported pf_hx_s_scld
## 80 sq_water_wellYes pf_hx_s_scld
## 81 sq_water_tap_unfilteredUnknown/Not Reported pf_hx_s_scld
## 82 sq_water_tap_unfilteredYes pf_hx_s_scld
## 83 sq_water_house_filtrationUnknown/Not Reported pf_hx_s_scld
## 84 sq_water_house_filtrationYes pf_hx_s_scld
## 85 sq_water_faucet_filterUnknown/Not Reported pf_hx_s_scld
## 86 sq_water_faucet_filterYes pf_hx_s_scld
## 87 sq_water_charcoal_filterUnknown/Not Reported pf_hx_s_scld
## 88 sq_water_charcoal_filterYes pf_hx_s_scld
## 89 sq_water_bottledUnknown/Not Reported pf_hx_s_scld
## 90 sq_water_bottledYes pf_hx_s_scld
## 91 sq_water_noneUnknown/Not Reported pf_hx_s_scld
## 92 sq_water_noneYes pf_hx_s_scld
## 93 sq_water_other_typeUnknown/Not Reported pf_hx_s_scld
## 94 sq_water_other_typeYes pf_hx_s_scld
## 95 sourceDUKE pfda_scld
## 96 sourceNCSU pfda_scld
## 97 sourceUNC pfda_scld
## 98 sexMale pfda_scld
## 99 race_eth_labelNHB pfda_scld
## 100 race_eth_labelNHO pfda_scld
## 101 race_eth_labelNHW pfda_scld
## 102 race_eth_labelUnknown/Not Reported pfda_scld
## 103 race_final_labelAmerican Indian pfda_scld
## 104 race_final_labelAmerican Indian/Alaskan Native pfda_scld
## 105 race_final_labelAsian pfda_scld
## 106 race_final_labelAsian/Pacific Islander pfda_scld
## 107 race_final_labelBlack pfda_scld
## 108 race_final_labelMore than one race pfda_scld
## 109 race_final_labelOther pfda_scld
## 110 race_final_labelUnknown/Not Reported pfda_scld
## 111 ethnicityNot Hispanic pfda_scld
## 112 ethnicityUnknown/Not Reported pfda_scld
## 113 ruralLiving in rural area pfda_scld
## 114 ruralUnknown/Not Reported pfda_scld
## 115 smokingSmoke or use vape pfda_scld
## 116 smokingUnknown/Not Reported pfda_scld
## 117 sq_drink_alcoholNo, former drinker (stopped) pfda_scld
## 118 sq_drink_alcoholUnknown/Not Reported pfda_scld
## 119 sq_drink_alcoholYes, current drinker pfda_scld
## 120 sq_average_drink_per_day1-2 alcoholic drinks per day pfda_scld
## 121 sq_average_drink_per_day3-4 alcoholic drinks per day pfda_scld
## 122 sq_average_drink_per_dayUnknown/Not Reported pfda_scld
## 123 sq_self_hep_bUnknown/Not Reported pfda_scld
## 124 sq_self_hep_bYes pfda_scld
## 125 sq_self_hep_cUnknown/Not Reported pfda_scld
## 126 sq_self_hep_cYes pfda_scld
## 127 supp_meds_tylenolUnknown/Not Reported pfda_scld
## 128 supp_meds_tylenolYes pfda_scld
## 129 supp_meds_steroidsUnknown/Not Reported pfda_scld
## 130 supp_meds_steroidsYes pfda_scld
## 131 sq_water_wellUnknown/Not Reported pfda_scld
## 132 sq_water_wellYes pfda_scld
## 133 sq_water_tap_unfilteredUnknown/Not Reported pfda_scld
## 134 sq_water_tap_unfilteredYes pfda_scld
## 135 sq_water_house_filtrationUnknown/Not Reported pfda_scld
## 136 sq_water_house_filtrationYes pfda_scld
## 137 sq_water_faucet_filterUnknown/Not Reported pfda_scld
## 138 sq_water_faucet_filterYes pfda_scld
## 139 sq_water_charcoal_filterUnknown/Not Reported pfda_scld
## 140 sq_water_charcoal_filterYes pfda_scld
## 141 sq_water_bottledUnknown/Not Reported pfda_scld
## 142 sq_water_bottledYes pfda_scld
## 143 sq_water_noneUnknown/Not Reported pfda_scld
## 144 sq_water_noneYes pfda_scld
## 145 sq_water_other_typeUnknown/Not Reported pfda_scld
## 146 sq_water_other_typeYes pfda_scld
## 147 sourceDUKE pfna_scld
## 148 sourceNCSU pfna_scld
## 149 sourceUNC pfna_scld
## 150 sexMale pfna_scld
## 151 race_eth_labelNHB pfna_scld
## 152 race_eth_labelNHO pfna_scld
## 153 race_eth_labelNHW pfna_scld
## 154 race_eth_labelUnknown/Not Reported pfna_scld
## 155 race_final_labelAmerican Indian pfna_scld
## 156 race_final_labelAmerican Indian/Alaskan Native pfna_scld
## 157 race_final_labelAsian pfna_scld
## 158 race_final_labelAsian/Pacific Islander pfna_scld
## 159 race_final_labelBlack pfna_scld
## 160 race_final_labelMore than one race pfna_scld
## 161 race_final_labelOther pfna_scld
## 162 race_final_labelUnknown/Not Reported pfna_scld
## 163 ethnicityNot Hispanic pfna_scld
## 164 ethnicityUnknown/Not Reported pfna_scld
## 165 ruralLiving in rural area pfna_scld
## 166 ruralUnknown/Not Reported pfna_scld
## 167 smokingSmoke or use vape pfna_scld
## 168 smokingUnknown/Not Reported pfna_scld
## 169 sq_drink_alcoholNo, former drinker (stopped) pfna_scld
## 170 sq_drink_alcoholUnknown/Not Reported pfna_scld
## 171 sq_drink_alcoholYes, current drinker pfna_scld
## 172 sq_average_drink_per_day1-2 alcoholic drinks per day pfna_scld
## 173 sq_average_drink_per_day3-4 alcoholic drinks per day pfna_scld
## 174 sq_average_drink_per_dayUnknown/Not Reported pfna_scld
## 175 sq_self_hep_bUnknown/Not Reported pfna_scld
## 176 sq_self_hep_bYes pfna_scld
## 177 sq_self_hep_cUnknown/Not Reported pfna_scld
## 178 sq_self_hep_cYes pfna_scld
## 179 supp_meds_tylenolUnknown/Not Reported pfna_scld
## 180 supp_meds_tylenolYes pfna_scld
## 181 supp_meds_steroidsUnknown/Not Reported pfna_scld
## 182 supp_meds_steroidsYes pfna_scld
## 183 sq_water_wellUnknown/Not Reported pfna_scld
## 184 sq_water_wellYes pfna_scld
## 185 sq_water_tap_unfilteredUnknown/Not Reported pfna_scld
## 186 sq_water_tap_unfilteredYes pfna_scld
## 187 sq_water_house_filtrationUnknown/Not Reported pfna_scld
## 188 sq_water_house_filtrationYes pfna_scld
## 189 sq_water_faucet_filterUnknown/Not Reported pfna_scld
## 190 sq_water_faucet_filterYes pfna_scld
## 191 sq_water_charcoal_filterUnknown/Not Reported pfna_scld
## 192 sq_water_charcoal_filterYes pfna_scld
## 193 sq_water_bottledUnknown/Not Reported pfna_scld
## 194 sq_water_bottledYes pfna_scld
## 195 sq_water_noneUnknown/Not Reported pfna_scld
## 196 sq_water_noneYes pfna_scld
## 197 sq_water_other_typeUnknown/Not Reported pfna_scld
## 198 sq_water_other_typeYes pfna_scld
## 199 sourceDUKE pfos_scld
## 200 sourceNCSU pfos_scld
## 201 sourceUNC pfos_scld
## 202 sexMale pfos_scld
## 203 race_eth_labelNHB pfos_scld
## 204 race_eth_labelNHO pfos_scld
## 205 race_eth_labelNHW pfos_scld
## 206 race_eth_labelUnknown/Not Reported pfos_scld
## 207 race_final_labelAmerican Indian pfos_scld
## 208 race_final_labelAmerican Indian/Alaskan Native pfos_scld
## 209 race_final_labelAsian pfos_scld
## 210 race_final_labelAsian/Pacific Islander pfos_scld
## 211 race_final_labelBlack pfos_scld
## 212 race_final_labelMore than one race pfos_scld
## 213 race_final_labelOther pfos_scld
## 214 race_final_labelUnknown/Not Reported pfos_scld
## 215 ethnicityNot Hispanic pfos_scld
## 216 ethnicityUnknown/Not Reported pfos_scld
## 217 ruralLiving in rural area pfos_scld
## 218 ruralUnknown/Not Reported pfos_scld
## 219 smokingSmoke or use vape pfos_scld
## 220 smokingUnknown/Not Reported pfos_scld
## 221 sq_drink_alcoholNo, former drinker (stopped) pfos_scld
## 222 sq_drink_alcoholUnknown/Not Reported pfos_scld
## 223 sq_drink_alcoholYes, current drinker pfos_scld
## 224 sq_average_drink_per_day1-2 alcoholic drinks per day pfos_scld
## 225 sq_average_drink_per_day3-4 alcoholic drinks per day pfos_scld
## 226 sq_average_drink_per_dayUnknown/Not Reported pfos_scld
## 227 sq_self_hep_bUnknown/Not Reported pfos_scld
## 228 sq_self_hep_bYes pfos_scld
## 229 sq_self_hep_cUnknown/Not Reported pfos_scld
## 230 sq_self_hep_cYes pfos_scld
## 231 supp_meds_tylenolUnknown/Not Reported pfos_scld
## 232 supp_meds_tylenolYes pfos_scld
## 233 supp_meds_steroidsUnknown/Not Reported pfos_scld
## 234 supp_meds_steroidsYes pfos_scld
## 235 sq_water_wellUnknown/Not Reported pfos_scld
## 236 sq_water_wellYes pfos_scld
## 237 sq_water_tap_unfilteredUnknown/Not Reported pfos_scld
## 238 sq_water_tap_unfilteredYes pfos_scld
## 239 sq_water_house_filtrationUnknown/Not Reported pfos_scld
## 240 sq_water_house_filtrationYes pfos_scld
## 241 sq_water_faucet_filterUnknown/Not Reported pfos_scld
## 242 sq_water_faucet_filterYes pfos_scld
## 243 sq_water_charcoal_filterUnknown/Not Reported pfos_scld
## 244 sq_water_charcoal_filterYes pfos_scld
## 245 sq_water_bottledUnknown/Not Reported pfos_scld
## 246 sq_water_bottledYes pfos_scld
## 247 sq_water_noneUnknown/Not Reported pfos_scld
## 248 sq_water_noneYes pfos_scld
## 249 sq_water_other_typeUnknown/Not Reported pfos_scld
## 250 sq_water_other_typeYes pfos_scld
## 251 sourceDUKE pf_hp_a_scld
## 252 sourceNCSU pf_hp_a_scld
## 253 sourceUNC pf_hp_a_scld
## 254 sexMale pf_hp_a_scld
## 255 race_eth_labelNHB pf_hp_a_scld
## 256 race_eth_labelNHO pf_hp_a_scld
## 257 race_eth_labelNHW pf_hp_a_scld
## 258 race_eth_labelUnknown/Not Reported pf_hp_a_scld
## 259 race_final_labelAmerican Indian pf_hp_a_scld
## 260 race_final_labelAmerican Indian/Alaskan Native pf_hp_a_scld
## 261 race_final_labelAsian pf_hp_a_scld
## 262 race_final_labelAsian/Pacific Islander pf_hp_a_scld
## 263 race_final_labelBlack pf_hp_a_scld
## 264 race_final_labelMore than one race pf_hp_a_scld
## 265 race_final_labelOther pf_hp_a_scld
## 266 race_final_labelUnknown/Not Reported pf_hp_a_scld
## 267 ethnicityNot Hispanic pf_hp_a_scld
## 268 ethnicityUnknown/Not Reported pf_hp_a_scld
## 269 ruralLiving in rural area pf_hp_a_scld
## 270 ruralUnknown/Not Reported pf_hp_a_scld
## 271 smokingSmoke or use vape pf_hp_a_scld
## 272 smokingUnknown/Not Reported pf_hp_a_scld
## 273 sq_drink_alcoholNo, former drinker (stopped) pf_hp_a_scld
## 274 sq_drink_alcoholUnknown/Not Reported pf_hp_a_scld
## 275 sq_drink_alcoholYes, current drinker pf_hp_a_scld
## 276 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_a_scld
## 277 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_a_scld
## 278 sq_average_drink_per_dayUnknown/Not Reported pf_hp_a_scld
## 279 sq_self_hep_bUnknown/Not Reported pf_hp_a_scld
## 280 sq_self_hep_bYes pf_hp_a_scld
## 281 sq_self_hep_cUnknown/Not Reported pf_hp_a_scld
## 282 sq_self_hep_cYes pf_hp_a_scld
## 283 supp_meds_tylenolUnknown/Not Reported pf_hp_a_scld
## 284 supp_meds_tylenolYes pf_hp_a_scld
## 285 supp_meds_steroidsUnknown/Not Reported pf_hp_a_scld
## 286 supp_meds_steroidsYes pf_hp_a_scld
## 287 sq_water_wellUnknown/Not Reported pf_hp_a_scld
## 288 sq_water_wellYes pf_hp_a_scld
## 289 sq_water_tap_unfilteredUnknown/Not Reported pf_hp_a_scld
## 290 sq_water_tap_unfilteredYes pf_hp_a_scld
## 291 sq_water_house_filtrationUnknown/Not Reported pf_hp_a_scld
## 292 sq_water_house_filtrationYes pf_hp_a_scld
## 293 sq_water_faucet_filterUnknown/Not Reported pf_hp_a_scld
## 294 sq_water_faucet_filterYes pf_hp_a_scld
## 295 sq_water_charcoal_filterUnknown/Not Reported pf_hp_a_scld
## 296 sq_water_charcoal_filterYes pf_hp_a_scld
## 297 sq_water_bottledUnknown/Not Reported pf_hp_a_scld
## 298 sq_water_bottledYes pf_hp_a_scld
## 299 sq_water_noneUnknown/Not Reported pf_hp_a_scld
## 300 sq_water_noneYes pf_hp_a_scld
## 301 sq_water_other_typeUnknown/Not Reported pf_hp_a_scld
## 302 sq_water_other_typeYes pf_hp_a_scld
## 303 sourceDUKE pfbs_scld
## 304 sourceNCSU pfbs_scld
## 305 sourceUNC pfbs_scld
## 306 sexMale pfbs_scld
## 307 race_eth_labelNHB pfbs_scld
## 308 race_eth_labelNHO pfbs_scld
## 309 race_eth_labelNHW pfbs_scld
## 310 race_eth_labelUnknown/Not Reported pfbs_scld
## 311 race_final_labelAmerican Indian pfbs_scld
## 312 race_final_labelAmerican Indian/Alaskan Native pfbs_scld
## 313 race_final_labelAsian pfbs_scld
## 314 race_final_labelAsian/Pacific Islander pfbs_scld
## 315 race_final_labelBlack pfbs_scld
## 316 race_final_labelMore than one race pfbs_scld
## 317 race_final_labelOther pfbs_scld
## 318 race_final_labelUnknown/Not Reported pfbs_scld
## 319 ethnicityNot Hispanic pfbs_scld
## 320 ethnicityUnknown/Not Reported pfbs_scld
## 321 ruralLiving in rural area pfbs_scld
## 322 ruralUnknown/Not Reported pfbs_scld
## 323 smokingSmoke or use vape pfbs_scld
## 324 smokingUnknown/Not Reported pfbs_scld
## 325 sq_drink_alcoholNo, former drinker (stopped) pfbs_scld
## 326 sq_drink_alcoholUnknown/Not Reported pfbs_scld
## 327 sq_drink_alcoholYes, current drinker pfbs_scld
## 328 sq_average_drink_per_day1-2 alcoholic drinks per day pfbs_scld
## 329 sq_average_drink_per_day3-4 alcoholic drinks per day pfbs_scld
## 330 sq_average_drink_per_dayUnknown/Not Reported pfbs_scld
## 331 sq_self_hep_bUnknown/Not Reported pfbs_scld
## 332 sq_self_hep_bYes pfbs_scld
## 333 sq_self_hep_cUnknown/Not Reported pfbs_scld
## 334 sq_self_hep_cYes pfbs_scld
## 335 supp_meds_tylenolUnknown/Not Reported pfbs_scld
## 336 supp_meds_tylenolYes pfbs_scld
## 337 supp_meds_steroidsUnknown/Not Reported pfbs_scld
## 338 supp_meds_steroidsYes pfbs_scld
## 339 sq_water_wellUnknown/Not Reported pfbs_scld
## 340 sq_water_wellYes pfbs_scld
## 341 sq_water_tap_unfilteredUnknown/Not Reported pfbs_scld
## 342 sq_water_tap_unfilteredYes pfbs_scld
## 343 sq_water_house_filtrationUnknown/Not Reported pfbs_scld
## 344 sq_water_house_filtrationYes pfbs_scld
## 345 sq_water_faucet_filterUnknown/Not Reported pfbs_scld
## 346 sq_water_faucet_filterYes pfbs_scld
## 347 sq_water_charcoal_filterUnknown/Not Reported pfbs_scld
## 348 sq_water_charcoal_filterYes pfbs_scld
## 349 sq_water_bottledUnknown/Not Reported pfbs_scld
## 350 sq_water_bottledYes pfbs_scld
## 351 sq_water_noneUnknown/Not Reported pfbs_scld
## 352 sq_water_noneYes pfbs_scld
## 353 sq_water_other_typeUnknown/Not Reported pfbs_scld
## 354 sq_water_other_typeYes pfbs_scld
## 355 sourceDUKE pfoa_scld
## 356 sourceNCSU pfoa_scld
## 357 sourceUNC pfoa_scld
## 358 sexMale pfoa_scld
## 359 race_eth_labelNHB pfoa_scld
## 360 race_eth_labelNHO pfoa_scld
## 361 race_eth_labelNHW pfoa_scld
## 362 race_eth_labelUnknown/Not Reported pfoa_scld
## 363 race_final_labelAmerican Indian pfoa_scld
## 364 race_final_labelAmerican Indian/Alaskan Native pfoa_scld
## 365 race_final_labelAsian pfoa_scld
## 366 race_final_labelAsian/Pacific Islander pfoa_scld
## 367 race_final_labelBlack pfoa_scld
## 368 race_final_labelMore than one race pfoa_scld
## 369 race_final_labelOther pfoa_scld
## 370 race_final_labelUnknown/Not Reported pfoa_scld
## 371 ethnicityNot Hispanic pfoa_scld
## 372 ethnicityUnknown/Not Reported pfoa_scld
## 373 ruralLiving in rural area pfoa_scld
## 374 ruralUnknown/Not Reported pfoa_scld
## 375 smokingSmoke or use vape pfoa_scld
## 376 smokingUnknown/Not Reported pfoa_scld
## 377 sq_drink_alcoholNo, former drinker (stopped) pfoa_scld
## 378 sq_drink_alcoholUnknown/Not Reported pfoa_scld
## 379 sq_drink_alcoholYes, current drinker pfoa_scld
## 380 sq_average_drink_per_day1-2 alcoholic drinks per day pfoa_scld
## 381 sq_average_drink_per_day3-4 alcoholic drinks per day pfoa_scld
## 382 sq_average_drink_per_dayUnknown/Not Reported pfoa_scld
## 383 sq_self_hep_bUnknown/Not Reported pfoa_scld
## 384 sq_self_hep_bYes pfoa_scld
## 385 sq_self_hep_cUnknown/Not Reported pfoa_scld
## 386 sq_self_hep_cYes pfoa_scld
## 387 supp_meds_tylenolUnknown/Not Reported pfoa_scld
## 388 supp_meds_tylenolYes pfoa_scld
## 389 supp_meds_steroidsUnknown/Not Reported pfoa_scld
## 390 supp_meds_steroidsYes pfoa_scld
## 391 sq_water_wellUnknown/Not Reported pfoa_scld
## 392 sq_water_wellYes pfoa_scld
## 393 sq_water_tap_unfilteredUnknown/Not Reported pfoa_scld
## 394 sq_water_tap_unfilteredYes pfoa_scld
## 395 sq_water_house_filtrationUnknown/Not Reported pfoa_scld
## 396 sq_water_house_filtrationYes pfoa_scld
## 397 sq_water_faucet_filterUnknown/Not Reported pfoa_scld
## 398 sq_water_faucet_filterYes pfoa_scld
## 399 sq_water_charcoal_filterUnknown/Not Reported pfoa_scld
## 400 sq_water_charcoal_filterYes pfoa_scld
## 401 sq_water_bottledUnknown/Not Reported pfoa_scld
## 402 sq_water_bottledYes pfoa_scld
## 403 sq_water_noneUnknown/Not Reported pfoa_scld
## 404 sq_water_noneYes pfoa_scld
## 405 sq_water_other_typeUnknown/Not Reported pfoa_scld
## 406 sq_water_other_typeYes pfoa_scld
## 407 sourceDUKE pf_pe_a_scld
## 408 sourceNCSU pf_pe_a_scld
## 409 sourceUNC pf_pe_a_scld
## 410 sexMale pf_pe_a_scld
## 411 race_eth_labelNHB pf_pe_a_scld
## 412 race_eth_labelNHO pf_pe_a_scld
## 413 race_eth_labelNHW pf_pe_a_scld
## 414 race_eth_labelUnknown/Not Reported pf_pe_a_scld
## 415 race_final_labelAmerican Indian pf_pe_a_scld
## 416 race_final_labelAmerican Indian/Alaskan Native pf_pe_a_scld
## 417 race_final_labelAsian pf_pe_a_scld
## 418 race_final_labelAsian/Pacific Islander pf_pe_a_scld
## 419 race_final_labelBlack pf_pe_a_scld
## 420 race_final_labelMore than one race pf_pe_a_scld
## 421 race_final_labelOther pf_pe_a_scld
## 422 race_final_labelUnknown/Not Reported pf_pe_a_scld
## 423 ethnicityNot Hispanic pf_pe_a_scld
## 424 ethnicityUnknown/Not Reported pf_pe_a_scld
## 425 ruralLiving in rural area pf_pe_a_scld
## 426 ruralUnknown/Not Reported pf_pe_a_scld
## 427 smokingSmoke or use vape pf_pe_a_scld
## 428 smokingUnknown/Not Reported pf_pe_a_scld
## 429 sq_drink_alcoholNo, former drinker (stopped) pf_pe_a_scld
## 430 sq_drink_alcoholUnknown/Not Reported pf_pe_a_scld
## 431 sq_drink_alcoholYes, current drinker pf_pe_a_scld
## 432 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_a_scld
## 433 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_a_scld
## 434 sq_average_drink_per_dayUnknown/Not Reported pf_pe_a_scld
## 435 sq_self_hep_bUnknown/Not Reported pf_pe_a_scld
## 436 sq_self_hep_bYes pf_pe_a_scld
## 437 sq_self_hep_cUnknown/Not Reported pf_pe_a_scld
## 438 sq_self_hep_cYes pf_pe_a_scld
## 439 supp_meds_tylenolUnknown/Not Reported pf_pe_a_scld
## 440 supp_meds_tylenolYes pf_pe_a_scld
## 441 supp_meds_steroidsUnknown/Not Reported pf_pe_a_scld
## 442 supp_meds_steroidsYes pf_pe_a_scld
## 443 sq_water_wellUnknown/Not Reported pf_pe_a_scld
## 444 sq_water_wellYes pf_pe_a_scld
## 445 sq_water_tap_unfilteredUnknown/Not Reported pf_pe_a_scld
## 446 sq_water_tap_unfilteredYes pf_pe_a_scld
## 447 sq_water_house_filtrationUnknown/Not Reported pf_pe_a_scld
## 448 sq_water_house_filtrationYes pf_pe_a_scld
## 449 sq_water_faucet_filterUnknown/Not Reported pf_pe_a_scld
## 450 sq_water_faucet_filterYes pf_pe_a_scld
## 451 sq_water_charcoal_filterUnknown/Not Reported pf_pe_a_scld
## 452 sq_water_charcoal_filterYes pf_pe_a_scld
## 453 sq_water_bottledUnknown/Not Reported pf_pe_a_scld
## 454 sq_water_bottledYes pf_pe_a_scld
## 455 sq_water_noneUnknown/Not Reported pf_pe_a_scld
## 456 sq_water_noneYes pf_pe_a_scld
## 457 sq_water_other_typeUnknown/Not Reported pf_pe_a_scld
## 458 sq_water_other_typeYes pf_pe_a_scld
## 459 sourceDUKE pf_un_a_scld
## 460 sourceNCSU pf_un_a_scld
## 461 sourceUNC pf_un_a_scld
## 462 sexMale pf_un_a_scld
## 463 race_eth_labelNHB pf_un_a_scld
## 464 race_eth_labelNHO pf_un_a_scld
## 465 race_eth_labelNHW pf_un_a_scld
## 466 race_eth_labelUnknown/Not Reported pf_un_a_scld
## 467 race_final_labelAmerican Indian pf_un_a_scld
## 468 race_final_labelAmerican Indian/Alaskan Native pf_un_a_scld
## 469 race_final_labelAsian pf_un_a_scld
## 470 race_final_labelAsian/Pacific Islander pf_un_a_scld
## 471 race_final_labelBlack pf_un_a_scld
## 472 race_final_labelMore than one race pf_un_a_scld
## 473 race_final_labelOther pf_un_a_scld
## 474 race_final_labelUnknown/Not Reported pf_un_a_scld
## 475 ethnicityNot Hispanic pf_un_a_scld
## 476 ethnicityUnknown/Not Reported pf_un_a_scld
## 477 ruralLiving in rural area pf_un_a_scld
## 478 ruralUnknown/Not Reported pf_un_a_scld
## 479 smokingSmoke or use vape pf_un_a_scld
## 480 smokingUnknown/Not Reported pf_un_a_scld
## 481 sq_drink_alcoholNo, former drinker (stopped) pf_un_a_scld
## 482 sq_drink_alcoholUnknown/Not Reported pf_un_a_scld
## 483 sq_drink_alcoholYes, current drinker pf_un_a_scld
## 484 sq_average_drink_per_day1-2 alcoholic drinks per day pf_un_a_scld
## 485 sq_average_drink_per_day3-4 alcoholic drinks per day pf_un_a_scld
## 486 sq_average_drink_per_dayUnknown/Not Reported pf_un_a_scld
## 487 sq_self_hep_bUnknown/Not Reported pf_un_a_scld
## 488 sq_self_hep_bYes pf_un_a_scld
## 489 sq_self_hep_cUnknown/Not Reported pf_un_a_scld
## 490 sq_self_hep_cYes pf_un_a_scld
## 491 supp_meds_tylenolUnknown/Not Reported pf_un_a_scld
## 492 supp_meds_tylenolYes pf_un_a_scld
## 493 supp_meds_steroidsUnknown/Not Reported pf_un_a_scld
## 494 supp_meds_steroidsYes pf_un_a_scld
## 495 sq_water_wellUnknown/Not Reported pf_un_a_scld
## 496 sq_water_wellYes pf_un_a_scld
## 497 sq_water_tap_unfilteredUnknown/Not Reported pf_un_a_scld
## 498 sq_water_tap_unfilteredYes pf_un_a_scld
## 499 sq_water_house_filtrationUnknown/Not Reported pf_un_a_scld
## 500 sq_water_house_filtrationYes pf_un_a_scld
## 501 sq_water_faucet_filterUnknown/Not Reported pf_un_a_scld
## 502 sq_water_faucet_filterYes pf_un_a_scld
## 503 sq_water_charcoal_filterUnknown/Not Reported pf_un_a_scld
## 504 sq_water_charcoal_filterYes pf_un_a_scld
## 505 sq_water_bottledUnknown/Not Reported pf_un_a_scld
## 506 sq_water_bottledYes pf_un_a_scld
## 507 sq_water_noneUnknown/Not Reported pf_un_a_scld
## 508 sq_water_noneYes pf_un_a_scld
## 509 sq_water_other_typeUnknown/Not Reported pf_un_a_scld
## 510 sq_water_other_typeYes pf_un_a_scld
## 511 sourceDUKE pf_hp_s_scld
## 512 sourceNCSU pf_hp_s_scld
## 513 sourceUNC pf_hp_s_scld
## 514 sexMale pf_hp_s_scld
## 515 race_eth_labelNHB pf_hp_s_scld
## 516 race_eth_labelNHO pf_hp_s_scld
## 517 race_eth_labelNHW pf_hp_s_scld
## 518 race_eth_labelUnknown/Not Reported pf_hp_s_scld
## 519 race_final_labelAmerican Indian pf_hp_s_scld
## 520 race_final_labelAmerican Indian/Alaskan Native pf_hp_s_scld
## 521 race_final_labelAsian pf_hp_s_scld
## 522 race_final_labelAsian/Pacific Islander pf_hp_s_scld
## 523 race_final_labelBlack pf_hp_s_scld
## 524 race_final_labelMore than one race pf_hp_s_scld
## 525 race_final_labelOther pf_hp_s_scld
## 526 race_final_labelUnknown/Not Reported pf_hp_s_scld
## 527 ethnicityNot Hispanic pf_hp_s_scld
## 528 ethnicityUnknown/Not Reported pf_hp_s_scld
## 529 ruralLiving in rural area pf_hp_s_scld
## 530 ruralUnknown/Not Reported pf_hp_s_scld
## 531 smokingSmoke or use vape pf_hp_s_scld
## 532 smokingUnknown/Not Reported pf_hp_s_scld
## 533 sq_drink_alcoholNo, former drinker (stopped) pf_hp_s_scld
## 534 sq_drink_alcoholUnknown/Not Reported pf_hp_s_scld
## 535 sq_drink_alcoholYes, current drinker pf_hp_s_scld
## 536 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_s_scld
## 537 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_s_scld
## 538 sq_average_drink_per_dayUnknown/Not Reported pf_hp_s_scld
## 539 sq_self_hep_bUnknown/Not Reported pf_hp_s_scld
## 540 sq_self_hep_bYes pf_hp_s_scld
## 541 sq_self_hep_cUnknown/Not Reported pf_hp_s_scld
## 542 sq_self_hep_cYes pf_hp_s_scld
## 543 supp_meds_tylenolUnknown/Not Reported pf_hp_s_scld
## 544 supp_meds_tylenolYes pf_hp_s_scld
## 545 supp_meds_steroidsUnknown/Not Reported pf_hp_s_scld
## 546 supp_meds_steroidsYes pf_hp_s_scld
## 547 sq_water_wellUnknown/Not Reported pf_hp_s_scld
## 548 sq_water_wellYes pf_hp_s_scld
## 549 sq_water_tap_unfilteredUnknown/Not Reported pf_hp_s_scld
## 550 sq_water_tap_unfilteredYes pf_hp_s_scld
## 551 sq_water_house_filtrationUnknown/Not Reported pf_hp_s_scld
## 552 sq_water_house_filtrationYes pf_hp_s_scld
## 553 sq_water_faucet_filterUnknown/Not Reported pf_hp_s_scld
## 554 sq_water_faucet_filterYes pf_hp_s_scld
## 555 sq_water_charcoal_filterUnknown/Not Reported pf_hp_s_scld
## 556 sq_water_charcoal_filterYes pf_hp_s_scld
## 557 sq_water_bottledUnknown/Not Reported pf_hp_s_scld
## 558 sq_water_bottledYes pf_hp_s_scld
## 559 sq_water_noneUnknown/Not Reported pf_hp_s_scld
## 560 sq_water_noneYes pf_hp_s_scld
## 561 sq_water_other_typeUnknown/Not Reported pf_hp_s_scld
## 562 sq_water_other_typeYes pf_hp_s_scld
## 563 sourceDUKE pf_do_a_scld
## 564 sourceNCSU pf_do_a_scld
## 565 sourceUNC pf_do_a_scld
## 566 sexMale pf_do_a_scld
## 567 race_eth_labelNHB pf_do_a_scld
## 568 race_eth_labelNHO pf_do_a_scld
## 569 race_eth_labelNHW pf_do_a_scld
## 570 race_eth_labelUnknown/Not Reported pf_do_a_scld
## 571 race_final_labelAmerican Indian pf_do_a_scld
## 572 race_final_labelAmerican Indian/Alaskan Native pf_do_a_scld
## 573 race_final_labelAsian pf_do_a_scld
## 574 race_final_labelAsian/Pacific Islander pf_do_a_scld
## 575 race_final_labelBlack pf_do_a_scld
## 576 race_final_labelMore than one race pf_do_a_scld
## 577 race_final_labelOther pf_do_a_scld
## 578 race_final_labelUnknown/Not Reported pf_do_a_scld
## 579 ethnicityNot Hispanic pf_do_a_scld
## 580 ethnicityUnknown/Not Reported pf_do_a_scld
## 581 ruralLiving in rural area pf_do_a_scld
## 582 ruralUnknown/Not Reported pf_do_a_scld
## 583 smokingSmoke or use vape pf_do_a_scld
## 584 smokingUnknown/Not Reported pf_do_a_scld
## 585 sq_drink_alcoholNo, former drinker (stopped) pf_do_a_scld
## 586 sq_drink_alcoholUnknown/Not Reported pf_do_a_scld
## 587 sq_drink_alcoholYes, current drinker pf_do_a_scld
## 588 sq_average_drink_per_day1-2 alcoholic drinks per day pf_do_a_scld
## 589 sq_average_drink_per_day3-4 alcoholic drinks per day pf_do_a_scld
## 590 sq_average_drink_per_dayUnknown/Not Reported pf_do_a_scld
## 591 sq_self_hep_bUnknown/Not Reported pf_do_a_scld
## 592 sq_self_hep_bYes pf_do_a_scld
## 593 sq_self_hep_cUnknown/Not Reported pf_do_a_scld
## 594 sq_self_hep_cYes pf_do_a_scld
## 595 supp_meds_tylenolUnknown/Not Reported pf_do_a_scld
## 596 supp_meds_tylenolYes pf_do_a_scld
## 597 supp_meds_steroidsUnknown/Not Reported pf_do_a_scld
## 598 supp_meds_steroidsYes pf_do_a_scld
## 599 sq_water_wellUnknown/Not Reported pf_do_a_scld
## 600 sq_water_wellYes pf_do_a_scld
## 601 sq_water_tap_unfilteredUnknown/Not Reported pf_do_a_scld
## 602 sq_water_tap_unfilteredYes pf_do_a_scld
## 603 sq_water_house_filtrationUnknown/Not Reported pf_do_a_scld
## 604 sq_water_house_filtrationYes pf_do_a_scld
## 605 sq_water_faucet_filterUnknown/Not Reported pf_do_a_scld
## 606 sq_water_faucet_filterYes pf_do_a_scld
## 607 sq_water_charcoal_filterUnknown/Not Reported pf_do_a_scld
## 608 sq_water_charcoal_filterYes pf_do_a_scld
## 609 sq_water_bottledUnknown/Not Reported pf_do_a_scld
## 610 sq_water_bottledYes pf_do_a_scld
## 611 sq_water_noneUnknown/Not Reported pf_do_a_scld
## 612 sq_water_noneYes pf_do_a_scld
## 613 sq_water_other_typeUnknown/Not Reported pf_do_a_scld
## 614 sq_water_other_typeYes pf_do_a_scld
## 615 sourceDUKE pf_pe_s_scld
## 616 sourceNCSU pf_pe_s_scld
## 617 sourceUNC pf_pe_s_scld
## 618 sexMale pf_pe_s_scld
## 619 race_eth_labelNHB pf_pe_s_scld
## 620 race_eth_labelNHO pf_pe_s_scld
## 621 race_eth_labelNHW pf_pe_s_scld
## 622 race_eth_labelUnknown/Not Reported pf_pe_s_scld
## 623 race_final_labelAmerican Indian pf_pe_s_scld
## 624 race_final_labelAmerican Indian/Alaskan Native pf_pe_s_scld
## 625 race_final_labelAsian pf_pe_s_scld
## 626 race_final_labelAsian/Pacific Islander pf_pe_s_scld
## 627 race_final_labelBlack pf_pe_s_scld
## 628 race_final_labelMore than one race pf_pe_s_scld
## 629 race_final_labelOther pf_pe_s_scld
## 630 race_final_labelUnknown/Not Reported pf_pe_s_scld
## 631 ethnicityNot Hispanic pf_pe_s_scld
## 632 ethnicityUnknown/Not Reported pf_pe_s_scld
## 633 ruralLiving in rural area pf_pe_s_scld
## 634 ruralUnknown/Not Reported pf_pe_s_scld
## 635 smokingSmoke or use vape pf_pe_s_scld
## 636 smokingUnknown/Not Reported pf_pe_s_scld
## 637 sq_drink_alcoholNo, former drinker (stopped) pf_pe_s_scld
## 638 sq_drink_alcoholUnknown/Not Reported pf_pe_s_scld
## 639 sq_drink_alcoholYes, current drinker pf_pe_s_scld
## 640 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_s_scld
## 641 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_s_scld
## 642 sq_average_drink_per_dayUnknown/Not Reported pf_pe_s_scld
## 643 sq_self_hep_bUnknown/Not Reported pf_pe_s_scld
## 644 sq_self_hep_bYes pf_pe_s_scld
## 645 sq_self_hep_cUnknown/Not Reported pf_pe_s_scld
## 646 sq_self_hep_cYes pf_pe_s_scld
## 647 supp_meds_tylenolUnknown/Not Reported pf_pe_s_scld
## 648 supp_meds_tylenolYes pf_pe_s_scld
## 649 supp_meds_steroidsUnknown/Not Reported pf_pe_s_scld
## 650 supp_meds_steroidsYes pf_pe_s_scld
## 651 sq_water_wellUnknown/Not Reported pf_pe_s_scld
## 652 sq_water_wellYes pf_pe_s_scld
## 653 sq_water_tap_unfilteredUnknown/Not Reported pf_pe_s_scld
## 654 sq_water_tap_unfilteredYes pf_pe_s_scld
## 655 sq_water_house_filtrationUnknown/Not Reported pf_pe_s_scld
## 656 sq_water_house_filtrationYes pf_pe_s_scld
## 657 sq_water_faucet_filterUnknown/Not Reported pf_pe_s_scld
## 658 sq_water_faucet_filterYes pf_pe_s_scld
## 659 sq_water_charcoal_filterUnknown/Not Reported pf_pe_s_scld
## 660 sq_water_charcoal_filterYes pf_pe_s_scld
## 661 sq_water_bottledUnknown/Not Reported pf_pe_s_scld
## 662 sq_water_bottledYes pf_pe_s_scld
## 663 sq_water_noneUnknown/Not Reported pf_pe_s_scld
## 664 sq_water_noneYes pf_pe_s_scld
## 665 sq_water_other_typeUnknown/Not Reported pf_pe_s_scld
## 666 sq_water_other_typeYes pf_pe_s_scld
## 667 sourceDUKE pf_hx_a_scld
## 668 sourceNCSU pf_hx_a_scld
## 669 sourceUNC pf_hx_a_scld
## 670 sexMale pf_hx_a_scld
## 671 race_eth_labelNHB pf_hx_a_scld
## 672 race_eth_labelNHO pf_hx_a_scld
## 673 race_eth_labelNHW pf_hx_a_scld
## 674 race_eth_labelUnknown/Not Reported pf_hx_a_scld
## 675 race_final_labelAmerican Indian pf_hx_a_scld
## 676 race_final_labelAmerican Indian/Alaskan Native pf_hx_a_scld
## 677 race_final_labelAsian pf_hx_a_scld
## 678 race_final_labelAsian/Pacific Islander pf_hx_a_scld
## 679 race_final_labelBlack pf_hx_a_scld
## 680 race_final_labelMore than one race pf_hx_a_scld
## 681 race_final_labelOther pf_hx_a_scld
## 682 race_final_labelUnknown/Not Reported pf_hx_a_scld
## 683 ethnicityNot Hispanic pf_hx_a_scld
## 684 ethnicityUnknown/Not Reported pf_hx_a_scld
## 685 ruralLiving in rural area pf_hx_a_scld
## 686 ruralUnknown/Not Reported pf_hx_a_scld
## 687 smokingSmoke or use vape pf_hx_a_scld
## 688 smokingUnknown/Not Reported pf_hx_a_scld
## 689 sq_drink_alcoholNo, former drinker (stopped) pf_hx_a_scld
## 690 sq_drink_alcoholUnknown/Not Reported pf_hx_a_scld
## 691 sq_drink_alcoholYes, current drinker pf_hx_a_scld
## 692 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_a_scld
## 693 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_a_scld
## 694 sq_average_drink_per_dayUnknown/Not Reported pf_hx_a_scld
## 695 sq_self_hep_bUnknown/Not Reported pf_hx_a_scld
## 696 sq_self_hep_bYes pf_hx_a_scld
## 697 sq_self_hep_cUnknown/Not Reported pf_hx_a_scld
## 698 sq_self_hep_cYes pf_hx_a_scld
## 699 supp_meds_tylenolUnknown/Not Reported pf_hx_a_scld
## 700 supp_meds_tylenolYes pf_hx_a_scld
## 701 supp_meds_steroidsUnknown/Not Reported pf_hx_a_scld
## 702 supp_meds_steroidsYes pf_hx_a_scld
## 703 sq_water_wellUnknown/Not Reported pf_hx_a_scld
## 704 sq_water_wellYes pf_hx_a_scld
## 705 sq_water_tap_unfilteredUnknown/Not Reported pf_hx_a_scld
## 706 sq_water_tap_unfilteredYes pf_hx_a_scld
## 707 sq_water_house_filtrationUnknown/Not Reported pf_hx_a_scld
## 708 sq_water_house_filtrationYes pf_hx_a_scld
## 709 sq_water_faucet_filterUnknown/Not Reported pf_hx_a_scld
## 710 sq_water_faucet_filterYes pf_hx_a_scld
## 711 sq_water_charcoal_filterUnknown/Not Reported pf_hx_a_scld
## 712 sq_water_charcoal_filterYes pf_hx_a_scld
## 713 sq_water_bottledUnknown/Not Reported pf_hx_a_scld
## 714 sq_water_bottledYes pf_hx_a_scld
## 715 sq_water_noneUnknown/Not Reported pf_hx_a_scld
## 716 sq_water_noneYes pf_hx_a_scld
## 717 sq_water_other_typeUnknown/Not Reported pf_hx_a_scld
## 718 sq_water_other_typeYes pf_hx_a_scld
## 719 sourceDUKE pfba_scld
## 720 sourceNCSU pfba_scld
## 721 sourceUNC pfba_scld
## 722 sexMale pfba_scld
## 723 race_eth_labelNHB pfba_scld
## 724 race_eth_labelNHO pfba_scld
## 725 race_eth_labelNHW pfba_scld
## 726 race_eth_labelUnknown/Not Reported pfba_scld
## 727 race_final_labelAmerican Indian pfba_scld
## 728 race_final_labelAmerican Indian/Alaskan Native pfba_scld
## 729 race_final_labelAsian pfba_scld
## 730 race_final_labelAsian/Pacific Islander pfba_scld
## 731 race_final_labelBlack pfba_scld
## 732 race_final_labelMore than one race pfba_scld
## 733 race_final_labelOther pfba_scld
## 734 race_final_labelUnknown/Not Reported pfba_scld
## 735 ethnicityNot Hispanic pfba_scld
## 736 ethnicityUnknown/Not Reported pfba_scld
## 737 ruralLiving in rural area pfba_scld
## 738 ruralUnknown/Not Reported pfba_scld
## 739 smokingSmoke or use vape pfba_scld
## 740 smokingUnknown/Not Reported pfba_scld
## 741 sq_drink_alcoholNo, former drinker (stopped) pfba_scld
## 742 sq_drink_alcoholUnknown/Not Reported pfba_scld
## 743 sq_drink_alcoholYes, current drinker pfba_scld
## 744 sq_average_drink_per_day1-2 alcoholic drinks per day pfba_scld
## 745 sq_average_drink_per_day3-4 alcoholic drinks per day pfba_scld
## 746 sq_average_drink_per_dayUnknown/Not Reported pfba_scld
## 747 sq_self_hep_bUnknown/Not Reported pfba_scld
## 748 sq_self_hep_bYes pfba_scld
## 749 sq_self_hep_cUnknown/Not Reported pfba_scld
## 750 sq_self_hep_cYes pfba_scld
## 751 supp_meds_tylenolUnknown/Not Reported pfba_scld
## 752 supp_meds_tylenolYes pfba_scld
## 753 supp_meds_steroidsUnknown/Not Reported pfba_scld
## 754 supp_meds_steroidsYes pfba_scld
## 755 sq_water_wellUnknown/Not Reported pfba_scld
## 756 sq_water_wellYes pfba_scld
## 757 sq_water_tap_unfilteredUnknown/Not Reported pfba_scld
## 758 sq_water_tap_unfilteredYes pfba_scld
## 759 sq_water_house_filtrationUnknown/Not Reported pfba_scld
## 760 sq_water_house_filtrationYes pfba_scld
## 761 sq_water_faucet_filterUnknown/Not Reported pfba_scld
## 762 sq_water_faucet_filterYes pfba_scld
## 763 sq_water_charcoal_filterUnknown/Not Reported pfba_scld
## 764 sq_water_charcoal_filterYes pfba_scld
## 765 sq_water_bottledUnknown/Not Reported pfba_scld
## 766 sq_water_bottledYes pfba_scld
## 767 sq_water_noneUnknown/Not Reported pfba_scld
## 768 sq_water_noneYes pfba_scld
## 769 sq_water_other_typeUnknown/Not Reported pfba_scld
## 770 sq_water_other_typeYes pfba_scld##
ggplot(all_results, aes(x = "", y = Coeff)) +
geom_boxplot(fill = "lightblue", color = "blue") +
labs(title = "Box Plot of Coefficients", y = "Coefficient") +
theme_minimal() 
##
all_results <- all_results %>%
mutate(
fancy_table <- set_caption(fancy_table, caption = "Confusion matrix results table (N=349)")
fancy_table <- fontsize(fancy_table, size = 8, part = "all") # Reduce font size of values
fancy_table <- fontsize(fancy_table, size = 9, part = "header") # Reduce font size of header
print(fancy_table)## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa`
## header has 1 row(s)
## body has 30 row(s)
## original dataset sample:
## Comparison
## lowest cut ast vs highest cut ast lowest cut ast vs highest cut ast
## lowest cut ast vs lowest cut alt lowest cut ast vs lowest cut alt
## lowest cut ast vs highest cut alt lowest cut ast vs highest cut alt
## lowest cut ast vs cirrhosis lowest cut ast vs cirrhosis
## lowest cut ast vs cirrhosis diagnosed lowest cut ast vs cirrhosis diagnosed
## Sensitivity Specificity PPV NPV
## lowest cut ast vs highest cut ast 0.743 1 1 0.690
## lowest cut ast vs lowest cut alt 0.221 1 1 0.423
## lowest cut ast vs highest cut alt 0.126 1 1 0.396
## lowest cut ast vs cirrhosis 0.968 0.008 0.630 0.125
## lowest cut ast vs cirrhosis diagnosed 0.498 0.752 0.783 0.455
## Accuracy F1_Score Kappa
## lowest cut ast vs highest cut ast 0.837 0.853 0
## lowest cut ast vs lowest cut alt 0.504 0.362 0
## lowest cut ast vs highest cut alt 0.444 0.224 0
## lowest cut ast vs cirrhosis 0.619 0.764 0
## lowest cut ast vs cirrhosis diagnosed 0.589 0.608 0#2. setting case_control as gold standard in total ppl
results <- list()
seen_comparisons <- character() # Initialize seen_comparisons as a character vector
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1', 'alt_cat2', 'cirrhosis', 'case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
variables <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')
# Identify the gold standard variable
gold_standard <- 'cirrhosis diagnosed'
for (i in seq_along(variables)) {
for (j in seq_along(variables)) {
if (i != j) {
# Define the comparison names
comparison_name_1 <- paste(variables[i], "vs", variables[j])
comparison_name_2 <- paste(variables[j], "vs", variables[i])
# Skip if the reverse comparison has already been processed
if (comparison_name_2 %in% seen_comparisons) {
next
}
# Mark the first comparison as seen
seen_comparisons <- c(seen_comparisons, comparison_name_1)
# Ensure both variables have the same length and no NAs
if (length(data_renamed[[variables[i]]]) == length(data_renamed[[variables[j]]])) {
# Remove rows with NA values in either variable
valid_indices <- complete.cases(data_renamed[[variables[i]]], data_renamed[[variables[j]]])
valid_data_i <- data_renamed[[variables[i]]][valid_indices]
valid_data_j <- data_renamed[[variables[j]]][valid_indices]
# Check if gold standard is involved
if (variables[j] == gold_standard) {
predicted <- valid_data_i
actual <- valid_data_j
} else if (variables[i] == gold_standard) {
predicted <- valid_data_j
actual <- valid_data_i
} else {
next
}
# Generate confusion matrix
conf_matrix <- table(predicted = predicted, actual = actual)
# Print confusion matrix for debugging
print(paste("Confusion Matrix for", comparison_name_1))
print(conf_matrix)
# Check for at least one Abnormal and one Normal class in both predicted and actual
levels_i <- rownames(conf_matrix)
levels_j <- colnames(conf_matrix)
if ("Abnormal" %in% levels_i && "Abnormal" %in% levels_j &&
"Normal" %in% levels_i && "Normal" %in% levels_j) {
# Extract values safely
true_pos <- conf_matrix["Abnormal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
false_neg <- conf_matrix["Abnormal", "Normal"] %>% ifelse(is.na(.), 0, .)
false_pos <- conf_matrix["Normal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
true_neg <- conf_matrix["Normal", "Normal"] %>% ifelse(is.na(.), 0, .)
# Calculate metrics
sensitivity <- true_pos / (true_pos + false_neg)
specificity <- true_neg / (true_neg + false_pos)
ppv <- true_pos / (true_pos + false_pos)
npv <- true_neg / (true_neg + false_neg)
accuracy <- (true_pos + true_neg) / sum(conf_matrix)
# Ensure predicted is numeric and actual is a factor
predicted_numeric <- as.numeric(factor(predicted, levels = unique(predicted)))
actual_factor <- factor(actual, levels = c("Normal", "Abnormal")) # Assuming "Abnormal" is positive class
# ROC Curve and AUC
roc_curve <- tryCatch({
roc(actual_factor, predicted_numeric)
}, error = function(e) {
print("Error in ROC calculation")
return(NULL)
})
auc_value <- if (!is.null(roc_curve)) auc(roc_curve) else NA
# F1 Score
f1_score <- if (!is.na(ppv + sensitivity) && ppv + sensitivity > 0) {
2 * (ppv * sensitivity) / (ppv + sensitivity)
} else {
NA
}
# Kappa Statistic
kappa_value <- tryCatch({
kappa2(conf_matrix)$value
}, error = function(e) {
print("Error in Kappa calculation")
return(NA)
})
# Store results
results[[comparison_name_1]] <- c(
Sensitivity = sensitivity,
Specificity = specificity,
PPV = ppv,
NPV = npv,
Accuracy = accuracy,
AUC = auc_value,
F1_Score = f1_score,
Kappa = kappa_value
)
} else {
results[[comparison_name_1]] <- c(
Sensitivity = NA,
Specificity = NA,
PPV = NA,
NPV = NA,
Accuracy = NA,
AUC = NA,
F1_Score = NA,
Kappa = NA
)
}
} else {
results[[comparison_name_1]] <- c(
Sensitivity = NA,
Specificity = NA,
PPV = NA,
NPV = NA,
Accuracy = NA,
AUC = NA,
F1_Score = NA,
Kappa = NA
)
}
}
}
}## [1] "Confusion Matrix for lowest cut ast vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 101 102
## Normal 28 85## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for highest cut ast vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 90 67
## Normal 39 120## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for lowest cut alt vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 25 22
## Normal 104 165## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for highest cut alt vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 14 14
## Normal 115 173## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for cirrhosis vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 128 180
## Normal 1 7## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases# Convert results to dataframe
results_df <- do.call(rbind, results)
results_df <- as.data.frame(results_df)
# Remove AUC column
results_df <- results_df %>% dplyr::select(-AUC)
# Format results
results_df <- results_df %>%
mutate(across(everything(), ~ case_when(
. == 1.000 ~ "1",
. == 0.000 ~ "0",
TRUE ~ sprintf("%.3f", .)
)))
results_df$Comparison <- rownames(results_df)
# Reorder columns
results_df <- results_df %>% dplyr::select(Comparison, everything())
# Filter to keep only the first comparison result (ignoring reversed comparisons)
results_df <- results_df %>%
filter(!grepl("vs", Comparison) | !duplicated(gsub("vs.*", "", Comparison)))
# Create flextable
fancy_table1 <- flextable(results_df)
fancy_table1 <- set_table_properties(fancy_table1, width = 0.8, layout = "autofit")
fancy_table1 <- set_caption(fancy_table1, caption = "Diagnosis of new categorical endpoints compared to case control status (gold standard) in total ppl (N=349)")
fancy_table1 <- fontsize(fancy_table1, size = 8, part = "all") # Reduce font size of values
fancy_table1 <- fontsize(fancy_table1, size = 9, part = "header") # Reduce font size of header
# Print flextable
print(fancy_table1)## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa`
## header has 1 row(s)
## body has 5 row(s)
## original dataset sample:
## Comparison
## lowest cut ast vs cirrhosis diagnosed lowest cut ast vs cirrhosis diagnosed
## highest cut ast vs cirrhosis diagnosed highest cut ast vs cirrhosis diagnosed
## lowest cut alt vs cirrhosis diagnosed lowest cut alt vs cirrhosis diagnosed
## highest cut alt vs cirrhosis diagnosed highest cut alt vs cirrhosis diagnosed
## cirrhosis vs cirrhosis diagnosed cirrhosis vs cirrhosis diagnosed
## Sensitivity Specificity PPV NPV
## lowest cut ast vs cirrhosis diagnosed 0.498 0.752 0.783 0.455
## highest cut ast vs cirrhosis diagnosed 0.573 0.755 0.698 0.642
## lowest cut alt vs cirrhosis diagnosed 0.532 0.613 0.194 0.882
## highest cut alt vs cirrhosis diagnosed 0.500 0.601 0.109 0.925
## cirrhosis vs cirrhosis diagnosed 0.416 0.875 0.992 0.037
## Accuracy F1_Score Kappa
## lowest cut ast vs cirrhosis diagnosed 0.589 0.608 0
## highest cut ast vs cirrhosis diagnosed 0.665 0.629 0
## lowest cut alt vs cirrhosis diagnosed 0.601 0.284 0
## highest cut alt vs cirrhosis diagnosed 0.592 0.178 0.333
## cirrhosis vs cirrhosis diagnosed 0.427 0.586 0############### scatter plot
results_df <- results_df %>% dplyr::select(-Kappa)
results_long <- results_df %>%
pivot_longer(cols = -Comparison, names_to = "Metric", values_to = "Value")
results_long <- results_long %>%
mutate(Value = as.numeric(Value)) %>%
filter(!is.na(Value))
new_labels <- c(
"cirrhosis vs cirrhosis diagnosed" = "cirrhosis by ast/alt>1",
"highest cut alt vs cirrhosis diagnosed" = "highest cut alt",
"lowest cut alt vs cirrhosis diagnosed" = "lowest cut alt",
"highest cut ast vs cirrhosis diagnosed" = "highest cut ast",
"lowest cut ast vs cirrhosis diagnosed" = "lowest cut ast"
)
palette_colors <- brewer.pal(n = length(unique(results_long$Comparison)), name = "Set1")
# Recode the Comparison column
results_long <- results_long %>%
mutate(Comparison = recode(Comparison, !!!new_labels))
# Create the scatter plot
ggplot(results_long, aes(x = Value, y = Metric, color = Comparison, shape = Comparison)) +
geom_point(size = 3) +
labs(title = "Diagnostic testing in total",
x = "Scores",
y = "Measure of diagnostic test performance",
color = "Comparison",
shape = "Comparison") +
scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1)) + # Adjust x-axis
theme(axis.text.y = element_text(size = 10),
axis.text.x = element_text(size = 10),
axis.title = element_text(size = 12),
legend.position = "right") +
scale_shape_manual(values = 1:length(unique(results_long$Comparison))) +
scale_color_manual(values = palette_colors)
# 3. after excluding NCSU data
results <- list()
seen_comparisons <- character() # Initialize seen_comparisons as a character vector
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1', 'alt_cat2', 'cirrhosis', 'case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
variables <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')
# Identify the gold standard variable
gold_standard <- 'cirrhosis diagnosed'
for (i in seq_along(variables)) {
for (j in seq_along(variables)) {
if (i != j) {
# Define the comparison names
comparison_name_1 <- paste(variables[i], "vs", variables[j])
comparison_name_2 <- paste(variables[j], "vs", variables[i])
# Skip if the reverse comparison has already been processed
if (comparison_name_2 %in% seen_comparisons) {
next
}
# Mark the first comparison as seen
seen_comparisons <- c(seen_comparisons, comparison_name_1)
# Ensure both variables have the same length and no NAs
if (length(data_renamed[[variables[i]]]) == length(data_renamed[[variables[j]]])) {
# Remove rows with NA values in either variable
valid_indices <- complete.cases(data_renamed[[variables[i]]], data_renamed[[variables[j]]])
valid_data_i <- data_renamed[[variables[i]]][valid_indices]
valid_data_j <- data_renamed[[variables[j]]][valid_indices]
# Check if gold standard is involved
if (variables[j] == gold_standard) {
predicted <- valid_data_i
actual <- valid_data_j
} else if (variables[i] == gold_standard) {
predicted <- valid_data_j
actual <- valid_data_i
} else {
next
}
# Generate confusion matrix
conf_matrix <- table(predicted = predicted, actual = actual)
# Print confusion matrix for debugging
print(paste("Confusion Matrix for", comparison_name_1))
print(conf_matrix)
# Check for at least one Abnormal and one Normal class in both predicted and actual
levels_i <- rownames(conf_matrix)
levels_j <- colnames(conf_matrix)
if ("Abnormal" %in% levels_i && "Abnormal" %in% levels_j &&
"Normal" %in% levels_i && "Normal" %in% levels_j) {
# Extract values safely
true_pos <- conf_matrix["Abnormal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
false_neg <- conf_matrix["Abnormal", "Normal"] %>% ifelse(is.na(.), 0, .)
false_pos <- conf_matrix["Normal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
true_neg <- conf_matrix["Normal", "Normal"] %>% ifelse(is.na(.), 0, .)
# Calculate metrics
sensitivity <- true_pos / (true_pos + false_neg)
specificity <- true_neg / (true_neg + false_pos)
ppv <- true_pos / (true_pos + false_pos)
npv <- true_neg / (true_neg + false_neg)
accuracy <- (true_pos + true_neg) / sum(conf_matrix)
# Ensure predicted is numeric and actual is a factor
predicted_numeric <- as.numeric(factor(predicted, levels = unique(predicted)))
actual_factor <- factor(actual, levels = c("Normal", "Abnormal")) # Assuming "Abnormal" is positive class
# ROC Curve and AUC
roc_curve <- tryCatch({
roc(actual_factor, predicted_numeric)
}, error = function(e) {
print("Error in ROC calculation")
return(NULL)
})
auc_value <- if (!is.null(roc_curve)) auc(roc_curve) else NA
# F1 Score
f1_score <- if (!is.na(ppv + sensitivity) && ppv + sensitivity > 0) {
2 * (ppv * sensitivity) / (ppv + sensitivity)
} else {
NA
}
# Kappa Statistic
kappa_value <- tryCatch({
kappa2(conf_matrix)$value
}, error = function(e) {
print("Error in Kappa calculation")
return(NA)
})
# Store results
results[[comparison_name_1]] <- c(
Sensitivity = sensitivity,
Specificity = specificity,
PPV = ppv,
NPV = npv,
Accuracy = accuracy,
AUC = auc_value,
F1_Score = f1_score,
Kappa = kappa_value
)
} else {
results[[comparison_name_1]] <- c(
Sensitivity = NA,
Specificity = NA,
PPV = NA,
NPV = NA,
Accuracy = NA,
AUC = NA,
F1_Score = NA,
Kappa = NA
)
}
} else {
results[[comparison_name_1]] <- c(
Sensitivity = NA,
Specificity = NA,
PPV = NA,
NPV = NA,
Accuracy = NA,
AUC = NA,
F1_Score = NA,
Kappa = NA
)
}
}
}
}## [1] "Confusion Matrix for lowest cut ast vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 2 67
## Normal 0 71## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for highest cut ast vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 1 34
## Normal 1 104## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for lowest cut alt vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 0 8
## Normal 2 130## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for highest cut alt vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 0 5
## Normal 2 133## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases## [1] "Confusion Matrix for cirrhosis vs cirrhosis diagnosed"
## actual
## predicted Abnormal Normal
## Abnormal 2 135
## Normal 0 3## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases# Convert results to dataframe
results_df <- do.call(rbind, results)
results_df <- as.data.frame(results_df)
# Remove AUC column
results_df <- results_df %>% dplyr::select(-AUC)
# Format results
results_df <- results_df %>%
mutate(across(everything(), ~ case_when(
. == 1.000 ~ "1",
. == 0.000 ~ "0",
TRUE ~ sprintf("%.3f", .)
)))
results_df$Comparison <- rownames(results_df)
# Reorder columns
results_df <- results_df %>% dplyr::select(Comparison, everything())
# Filter to keep only the first comparison result (ignoring reversed comparisons)
results_df <- results_df %>%
filter(!grepl("vs", Comparison) | !duplicated(gsub("vs.*", "", Comparison)))
# Create flextable
fancy_table2 <- flextable(results_df)
fancy_table2 <- set_table_properties(fancy_table2, width = 0.8, layout = "autofit")
fancy_table2 <- set_caption(fancy_table2, caption = "Diagnosis of new categorical endpoints compared to case control status (gold standard) after excluding NCSU (N=173)")
fancy_table2 <- fontsize(fancy_table2, size = 8, part = "all") # Reduce font size of values
fancy_table2 <- fontsize(fancy_table2, size = 9, part = "header") # Reduce font size of header
# Print flextable
print(fancy_table2)## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa`
## header has 1 row(s)
## body has 5 row(s)
## original dataset sample:
## Comparison
## lowest cut ast vs cirrhosis diagnosed lowest cut ast vs cirrhosis diagnosed
## highest cut ast vs cirrhosis diagnosed highest cut ast vs cirrhosis diagnosed
## lowest cut alt vs cirrhosis diagnosed lowest cut alt vs cirrhosis diagnosed
## highest cut alt vs cirrhosis diagnosed highest cut alt vs cirrhosis diagnosed
## cirrhosis vs cirrhosis diagnosed cirrhosis vs cirrhosis diagnosed
## Sensitivity Specificity PPV NPV
## lowest cut ast vs cirrhosis diagnosed 0.029 1 1 0.514
## highest cut ast vs cirrhosis diagnosed 0.029 0.990 0.500 0.754
## lowest cut alt vs cirrhosis diagnosed 0 0.985 0 0.942
## highest cut alt vs cirrhosis diagnosed 0 0.985 0 0.964
## cirrhosis vs cirrhosis diagnosed 0.015 1 1 0.022
## Accuracy F1_Score Kappa
## lowest cut ast vs cirrhosis diagnosed 0.521 0.056 0
## highest cut ast vs cirrhosis diagnosed 0.750 0.054 0
## lowest cut alt vs cirrhosis diagnosed 0.929 NA 0
## highest cut alt vs cirrhosis diagnosed 0.950 NA 0
## cirrhosis vs cirrhosis diagnosed 0.036 0.029 0###
doc <- read_docx()
doc <- body_add_flextable(doc, fancy_table)
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_flextable(doc, fancy_table1)
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_flextable(doc, fancy_table2)
print(doc, target = "Categorical endpoint diagnosis.docx")
############### scatter plot
results_df <- results_df %>% dplyr::select(-Kappa)
results_long <- results_df %>%
pivot_longer(cols = -Comparison, names_to = "Metric", values_to = "Value")
results_long <- results_long %>%
mutate(Value = as.numeric(Value)) %>%
filter(!is.na(Value)) ## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Value = as.numeric(Value)`.
## Caused by warning:
## ! NAs introduced by coercionnew_labels <- c(
"cirrhosis vs cirrhosis diagnosed" = "cirrhosis by ast/alt>1",
"highest cut alt vs cirrhosis diagnosed" = "highest cut alt",
"lowest cut alt vs cirrhosis diagnosed" = "lowest cut alt",
"highest cut ast vs cirrhosis diagnosed" = "highest cut ast",
"lowest cut ast vs cirrhosis diagnosed" = "lowest cut ast"
)
palette_colors <- brewer.pal(n = length(unique(results_long$Comparison)), name = "Set1")
# Recode the Comparison column
results_long <- results_long %>%
mutate(Comparison = recode(Comparison, !!!new_labels))
# Create the scatter plot
ggplot(results_long, aes(x = Value, y = Metric, color = Comparison, shape = Comparison)) +
geom_point(size = 3) +
labs(title = "Diagnostic testing after excluding NCSU",
x = "Scores",
y = "Measure of diagnostic test performance",
color = "Comparison",
shape = "Comparison") +
scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1)) + # Adjust x-axis
theme(axis.text.y = element_text(size = 10),
axis.text.x = element_text(size = 10),
axis.title = element_text(size = 12),
legend.position = "right") +
scale_shape_manual(values = 1:length(unique(results_long$Comparison))) +
scale_color_manual(values = palette_colors)
Correlation heat map btw PFAS and potential confounders
# reset up reference category of some potential confounders
vars_to_convert <- c("source", "race_final_label","sq_drink_alcohol","sq_average_drink_per_day")
data[vars_to_convert] <- lapply(data[vars_to_convert], as.factor)
data$source <- relevel(data$source, ref = "Emory")
data$race_final_label <- relevel(data$race_final_label, ref = "White")
data$sq_drink_alcohol <- relevel(data$sq_drink_alcohol, ref = "No, never drinker")
data$sq_average_drink_per_day <- relevel(data$sq_average_drink_per_day, ref = "Less than 1 alcoholic drink per day")
# Define continuous potential confounders
numeric_data <- data %>%
dplyr::select(all_of(potential_conf)) %>%
dplyr::select_if(is.numeric)
conti_conf <- names(numeric_data)
# Function to extract model summary for continuous covariates
extract_model_summary <- function(pfas, covariate, data) {
model <- lm(as.formula(paste(pfas, "~", covariate)), data = data)
summary_model <- summary(model)
estimate <- coef(summary_model)[2, 1]
p_value <- coef(summary_model)[2, 4]
return(data.frame(Confounders = covariate, Coeff = estimate, P = p_value, Factor = covariate, PFAS = pfas))
}
# Apply to all PFAS variables and continuous covariates
continuous_results <- bind_rows(lapply(pfas_name_scld, function(pfas) {
bind_rows(lapply(conti_conf, function(cov) extract_model_summary(pfas, cov, data)))
}))
# Print continuous results
print(continuous_results)## Confounders Coeff P Factor
## 1 age_at_enrollment 0.02303963456 0.00002886969906048 age_at_enrollment
## 2 bmi -0.01294812141 0.09611802745084295 bmi
## 3 trig_mg_d_l 0.00006026119 0.92894071463283046 trig_mg_d_l
## 4 age_at_enrollment 0.01401689114 0.01151280974826796 age_at_enrollment
## 5 bmi -0.01953451965 0.01049324625441954 bmi
## 6 trig_mg_d_l -0.00087134772 0.19560135743360868 trig_mg_d_l
## 7 age_at_enrollment 0.02285242310 0.00003418692654294 age_at_enrollment
## 8 bmi -0.01016268100 0.19162630103330469 bmi
## 9 trig_mg_d_l -0.00012960498 0.84543151201780353 trig_mg_d_l
## 10 age_at_enrollment 0.03015972790 0.00000003849922364 age_at_enrollment
## 11 bmi -0.00632410823 0.39797483121906918 bmi
## 12 trig_mg_d_l -0.00060423845 0.36564464944825859 trig_mg_d_l
## 13 age_at_enrollment 0.00610856935 0.27012846280853531 age_at_enrollment
## 14 bmi -0.00353210464 0.63621285405847949 bmi
## 15 trig_mg_d_l -0.00037474559 0.57810255513241993 trig_mg_d_l
## 16 age_at_enrollment -0.00039411970 0.94418956366096030 age_at_enrollment
## 17 bmi 0.00294487490 0.67943161804413910 bmi
## 18 trig_mg_d_l -0.00051069442 0.45289086346786955 trig_mg_d_l
## 19 age_at_enrollment 0.01990379953 0.00032706545799936 age_at_enrollment
## 20 bmi -0.01850220830 0.01730193122393180 bmi
## 21 trig_mg_d_l 0.00025406806 0.70465471480120978 trig_mg_d_l
## 22 age_at_enrollment 0.00422728729 0.41715088028540981 age_at_enrollment
## 23 bmi -0.00405085209 0.60753830998090086 bmi
## 24 trig_mg_d_l -0.00053365608 0.39127771559889879 trig_mg_d_l
## 25 age_at_enrollment 0.01474075004 0.00829630670610244 age_at_enrollment
## 26 bmi -0.02788390573 0.00016089041529066 bmi
## 27 trig_mg_d_l -0.00105386273 0.11863558257777872 trig_mg_d_l
## 28 age_at_enrollment 0.03647003290 0.00000000001254972 age_at_enrollment
## 29 bmi -0.00498536122 0.49783998070122160 bmi
## 30 trig_mg_d_l 0.00035106604 0.59487932766741591 trig_mg_d_l
## 31 age_at_enrollment 0.00666942172 0.23478386028671305 age_at_enrollment
## 32 bmi -0.02691555479 0.00053014713321087 bmi
## 33 trig_mg_d_l -0.00088518107 0.19110820055049224 trig_mg_d_l
## 34 age_at_enrollment 0.00723125430 0.18396989965273475 age_at_enrollment
## 35 bmi -0.02551872568 0.00081610677927414 bmi
## 36 trig_mg_d_l -0.00011850725 0.86073672780481414 trig_mg_d_l
## 37 age_at_enrollment -0.00767217344 0.16771220778328327 age_at_enrollment
## 38 bmi 0.00258183929 0.73804930867047069 bmi
## 39 trig_mg_d_l -0.00082944038 0.21370329383697809 trig_mg_d_l
## 40 age_at_enrollment 0.00156141004 0.75969057121585326 age_at_enrollment
## 41 bmi -0.00261010632 0.73748118274195495 bmi
## 42 trig_mg_d_l -0.00015137036 0.80919075133267548 trig_mg_d_l
## PFAS
## 1 pf_hx_s_scld
## 2 pf_hx_s_scld
## 3 pf_hx_s_scld
## 4 pfda_scld
## 5 pfda_scld
## 6 pfda_scld
## 7 pfna_scld
## 8 pfna_scld
## 9 pfna_scld
## 10 pfos_scld
## 11 pfos_scld
## 12 pfos_scld
## 13 pf_hp_a_scld
## 14 pf_hp_a_scld
## 15 pf_hp_a_scld
## 16 pfbs_scld
## 17 pfbs_scld
## 18 pfbs_scld
## 19 pfoa_scld
## 20 pfoa_scld
## 21 pfoa_scld
## 22 pf_pe_a_scld
## 23 pf_pe_a_scld
## 24 pf_pe_a_scld
## 25 pf_un_a_scld
## 26 pf_un_a_scld
## 27 pf_un_a_scld
## 28 pf_hp_s_scld
## 29 pf_hp_s_scld
## 30 pf_hp_s_scld
## 31 pf_do_a_scld
## 32 pf_do_a_scld
## 33 pf_do_a_scld
## 34 pf_pe_s_scld
## 35 pf_pe_s_scld
## 36 pf_pe_s_scld
## 37 pf_hx_a_scld
## 38 pf_hx_a_scld
## 39 pf_hx_a_scld
## 40 pfba_scld
## 41 pfba_scld
## 42 pfba_scld# Define categorical confounders
cate_conf <- setdiff(potential_conf, conti_conf)
data <- data %>%
mutate(across(all_of(cate_conf), as.factor))
# Function to fit model and handle categorical variables
fit_model_cat <- function(pfas, covariate, data) {
model <- lm(as.formula(paste(pfas, "~", covariate)), data = data)
summary_model <- summary(model)
coefficients_df <- as.data.frame(summary_model$coefficients)
coefficients_df <- coefficients_df[-1, ] # Exclude intercept
results <- data.frame()
for (i in 1:nrow(coefficients_df)) {
current_result <- data.frame(
Confounders = covariate,
Coeff = coefficients_df[i, "Estimate"],
P = coefficients_df[i, "Pr(>|t|)"],
Factor = rownames(coefficients_df)[i],
PFAS = pfas
)
results <- rbind(results, current_result)
}
return(results)
}
# Apply to all PFAS variables and categorical covariates
categorical_results <- bind_rows(lapply(pfas_name_scld, function(pfas) {
bind_rows(lapply(cate_conf, function(cov) fit_model_cat(pfas, cov, data)))
}))
# Print categorical results
print(categorical_results)## Confounders Coeff P
## 1 source 0.200895798 0.17816637785406
## 2 source 0.287731004 0.02406241741592
## 3 source -0.240398916 0.34812012829126
## 4 sex 0.187197691 0.08219582251875
## 5 race_eth_label 0.050788492 0.84233629638901
## 6 race_eth_label -0.115104272 0.74149107535182
## 7 race_eth_label 0.274229818 0.26292213105599
## 8 race_eth_label 0.038885809 0.89888254812528
## 9 race_final_label -0.125411971 0.86037830965616
## 10 race_final_label -0.567654328 0.26261635127197
## 11 race_final_label -0.094110264 0.87184652297268
## 12 race_final_label -0.407973324 0.32604479871633
## 13 race_final_label -0.209886202 0.07137795642001
## 14 race_final_label -0.264366169 0.79275654669035
## 15 race_final_label -0.293394272 0.32387441802348
## 16 race_final_label -0.140730470 0.71483775593415
## 17 ethnicity 0.189177168 0.43536968718843
## 18 ethnicity 0.038885809 0.89927233218110
## 19 rural -0.246551067 0.18530426138296
## 20 rural -0.147877097 0.36453370981274
## 21 smoking -0.323810274 0.07305486987873
## 22 smoking -0.229485806 0.04908826819920
## 23 sq_drink_alcohol -0.091942208 0.55776878080577
## 24 sq_drink_alcohol -0.224849555 0.12238274703819
## 25 sq_drink_alcohol -0.005566960 0.96937817849958
## 26 sq_average_drink_per_day 0.116084716 0.65578352846414
## 27 sq_average_drink_per_day -0.261826993 0.53666952886876
## 28 sq_average_drink_per_day -0.105299660 0.40074255201137
## 29 sq_self_hep_b -0.141938612 0.21157942812386
## 30 sq_self_hep_b 0.192152070 0.44361557003896
## 31 sq_self_hep_c -0.146606869 0.19870154554602
## 32 sq_self_hep_c -0.155239261 0.52527191567200
## 33 supp_meds_tylenol 0.304940955 0.42547659640687
## 34 supp_meds_tylenol 0.419083324 0.50488516530130
## 35 supp_meds_steroids 0.040924100 0.90371151384786
## 36 supp_meds_steroids -0.657053844 0.53425913283768
## 37 sq_water_well -0.209121056 0.06225382264052
## 38 sq_water_well -0.218124485 0.15388307408752
## 39 sq_water_tap_unfiltered -0.023621223 0.86549139364515
## 40 sq_water_tap_unfiltered 0.242930596 0.06779584280842
## 41 sq_water_house_filtration -0.164768323 0.13133740209379
## 42 sq_water_house_filtration -0.015941373 0.93081104646968
## 43 sq_water_faucet_filter -0.137999833 0.25477840057300
## 44 sq_water_faucet_filter 0.058969782 0.65148799314024
## 45 sq_water_charcoal_filter -0.157835307 0.15697464465805
## 46 sq_water_charcoal_filter 0.078120988 0.61742210595572
## 47 sq_water_bottled -0.489245463 0.00049146516071
## 48 sq_water_bottled -0.425652852 0.00165296201584
## 49 sq_water_none -0.155707258 0.14497821238538
## 50 sq_water_none -0.401945941 0.10160522807343
## 51 sq_water_other_type -0.144238597 0.18408036183405
## 52 sq_water_other_type -0.171961692 0.40129154196832
## 53 source -0.246320491 0.09903735645484
## 54 source -0.089689998 0.48062363680237
## 55 source -0.664123896 0.00983867718366
## 56 sex -0.132717021 0.21831847824619
## 57 race_eth_label 0.124713526 0.62644193500231
## 58 race_eth_label 0.427845656 0.22209485749045
## 59 race_eth_label 0.219074045 0.37257706140279
## 60 race_eth_label -0.051398854 0.86705038235471
## 61 race_final_label -0.129041956 0.85548381268167
## 62 race_final_label -0.480227615 0.34013223492180
## 63 race_final_label 0.221625730 0.70227327313975
## 64 race_final_label 0.731311112 0.07689498379755
## 65 race_final_label -0.106015704 0.35862071309513
## 66 race_final_label 1.830449710 0.06776455127017
## 67 race_final_label -0.181875197 0.53812055995121
## 68 race_final_label -0.426284433 0.26576060685672
## 69 ethnicity 0.199915847 0.40915967427517
## 70 ethnicity -0.051398854 0.86694944980858
## 71 rural -0.223213006 0.23079719614026
## 72 rural -0.089919293 0.58161444509190
## 73 smoking -0.477786491 0.00796269280740
## 74 smoking -0.268518439 0.02067734372000
## 75 sq_drink_alcohol -0.207870279 0.17690069057810
## 76 sq_drink_alcohol -0.133574488 0.34875449247860
## 77 sq_drink_alcohol 0.343071427 0.01623256250437
## 78 sq_average_drink_per_day -0.065819563 0.79640617614151
## 79 sq_average_drink_per_day 0.309710825 0.45570924404055
## 80 sq_average_drink_per_day -0.449821686 0.00027963169345
## 81 sq_self_hep_b -0.195178728 0.08568444046719
## 82 sq_self_hep_b 0.124792657 0.61823627484439
## 83 sq_self_hep_c -0.188575571 0.09718837256862
## 84 sq_self_hep_c 0.293829902 0.22767940372033
## 85 supp_meds_tylenol -0.104426492 0.78494873278750
## 86 supp_meds_tylenol 0.137188835 0.82724883059650
## 87 supp_meds_steroids -0.209622339 0.53564564076427
## 88 supp_meds_steroids -0.222383053 0.83337724859734
## 89 sq_water_well -0.132687198 0.23787633145928
## 90 sq_water_well -0.097902158 0.52323933577829
## 91 sq_water_tap_unfiltered -0.071825166 0.60904913514096
## 92 sq_water_tap_unfiltered 0.047090239 0.72458169476826
## 93 sq_water_house_filtration -0.118485504 0.27832139702085
## 94 sq_water_house_filtration -0.069280874 0.70639209383939
## 95 sq_water_faucet_filter -0.111215023 0.35955630258943
## 96 sq_water_faucet_filter -0.003617243 0.97793718366327
## 97 sq_water_charcoal_filter -0.118284545 0.28976269305199
## 98 sq_water_charcoal_filter -0.030459708 0.84593988600084
## 99 sq_water_bottled -0.338884188 0.01623333367883
## 100 sq_water_bottled -0.326239577 0.01651137591491
## 101 sq_water_none -0.081531902 0.44683501274427
## 102 sq_water_none -0.161168516 0.51271635885510
## 103 sq_water_other_type -0.140356692 0.19632346742509
## 104 sq_water_other_type 0.005741845 0.97764091282822
## 105 source -0.175369787 0.24029175304655
## 106 source -0.001557516 0.99023454642434
## 107 source -0.604201367 0.01887733932903
## 108 sex -0.182491531 0.09022350808633
## 109 race_eth_label 0.111439098 0.66399374875135
## 110 race_eth_label 0.075383541 0.82971361421867
## 111 race_eth_label 0.235158486 0.33914499829761
## 112 race_eth_label 0.010202957 0.97352278690987
## 113 race_final_label -0.031005242 0.96535916817741
## 114 race_final_label -0.439367404 0.38636569597660
## 115 race_final_label -0.010423415 0.98576358357417
## 116 race_final_label -0.117074701 0.77822396547546
## 117 race_final_label -0.118282395 0.30947290327306
## 118 race_final_label 1.415335888 0.16063322156114
## 119 race_final_label -0.232301749 0.43522678347209
## 120 race_final_label -0.250188337 0.51661750631772
## 121 ethnicity 0.190570814 0.43185377958703
## 122 ethnicity 0.010202957 0.97349654239088
## 123 rural -0.166572585 0.37123860525779
## 124 rural -0.122277328 0.45397511461728
## 125 smoking -0.550497166 0.00222769302932
## 126 smoking -0.243033617 0.03579987987392
## 127 sq_drink_alcohol -0.152143531 0.32289373258877
## 128 sq_drink_alcohol -0.064578736 0.65051917779038
## 129 sq_drink_alcohol 0.395101987 0.00571563030052
## 130 sq_average_drink_per_day -0.179363348 0.48139369719177
## 131 sq_average_drink_per_day 0.339756072 0.41243345212406
## 132 sq_average_drink_per_day -0.475665430 0.00012072205421
## 133 sq_self_hep_b -0.169980812 0.13479409364304
## 134 sq_self_hep_b 0.077652727 0.75677422534222
## 135 sq_self_hep_c -0.153241706 0.17920855998162
## 136 sq_self_hep_c -0.041526636 0.86504317800144
## 137 supp_meds_tylenol 0.137831746 0.71761513785548
## 138 supp_meds_tylenol 1.045530092 0.09550981098713
## 139 supp_meds_steroids -0.308992929 0.36097388049451
## 140 supp_meds_steroids 0.101453855 0.92347692411989
## 141 sq_water_well -0.197910956 0.07794860503605
## 142 sq_water_well -0.145051446 0.34325528210842
## 143 sq_water_tap_unfiltered -0.066858483 0.63379349809383
## 144 sq_water_tap_unfiltered 0.077746821 0.56054814450977
## 145 sq_water_house_filtration -0.157196639 0.15011332667990
## 146 sq_water_house_filtration -0.063597366 0.72917960795944
## 147 sq_water_faucet_filter -0.167657482 0.16701503357658
## 148 sq_water_faucet_filter -0.062872816 0.63038370929638
## 149 sq_water_charcoal_filter -0.171482905 0.12453939373143
## 150 sq_water_charcoal_filter -0.088273734 0.57281724091754
## 151 sq_water_bottled -0.316950714 0.02481255320483
## 152 sq_water_bottled -0.257416023 0.05871655083207
## 153 sq_water_none -0.150395243 0.15986807660113
## 154 sq_water_none -0.280592971 0.25347435646347
## 155 sq_water_other_type -0.178793168 0.09958577804326
## 156 sq_water_other_type -0.063262997 0.75719660380251
## 157 source -0.028123983 0.85071911056610
## 158 source 0.065459503 0.60757504689612
## 159 source -0.549343879 0.03293039529831
## 160 sex 0.170180187 0.11421477651813
## 161 race_eth_label 0.177351427 0.48947961492581
## 162 race_eth_label 0.213688811 0.54222117666899
## 163 race_eth_label 0.220729261 0.36962368798292
## 164 race_eth_label -0.064254677 0.83445298522021
## 165 race_final_label -0.256501601 0.71719909410729
## 166 race_final_label -0.612124024 0.22393436309560
## 167 race_final_label 0.302218261 0.60200810793988
## 168 race_final_label 0.345060629 0.40285038365019
## 169 race_final_label -0.030336969 0.79256603065347
## 170 race_final_label 2.506331197 0.01250591825764
## 171 race_final_label -0.261501159 0.37590551468125
## 172 race_final_label -0.356932287 0.35102751784317
## 173 ethnicity 0.207295824 0.39191644876826
## 174 ethnicity -0.064254677 0.83404410390463
## 175 rural -0.150503790 0.41945767850033
## 176 rural -0.025677499 0.87510243365735
## 177 smoking -0.568161472 0.00162483462452
## 178 smoking -0.177642837 0.12484388703393
## 179 sq_drink_alcohol -0.092797903 0.55221901412800
## 180 sq_drink_alcohol -0.036172572 0.80250707464715
## 181 sq_drink_alcohol 0.250989822 0.08264764890084
## 182 sq_average_drink_per_day -0.147331304 0.56878498930872
## 183 sq_average_drink_per_day 0.137412767 0.74388410618384
## 184 sq_average_drink_per_day -0.309054336 0.01330787645467
## 185 sq_self_hep_b -0.096081724 0.39765419685309
## 186 sq_self_hep_b 0.273215641 0.27638203097615
## 187 sq_self_hep_c -0.088278490 0.43852931773111
## 188 sq_self_hep_c 0.266319451 0.27592232776750
## 189 supp_meds_tylenol 0.222254099 0.56129273508639
## 190 supp_meds_tylenol 0.490187456 0.43550301353661
## 191 supp_meds_steroids -0.001370466 0.99676971448587
## 192 supp_meds_steroids -0.083001213 0.93744563737665
## 193 sq_water_well -0.138214358 0.21869101547987
## 194 sq_water_well -0.146054748 0.34083898263585
## 195 sq_water_tap_unfiltered -0.108841289 0.43862900238366
## 196 sq_water_tap_unfiltered -0.031520281 0.81362118915628
## 197 sq_water_house_filtration -0.101441951 0.35332742478950
## 198 sq_water_house_filtration 0.028812444 0.87552600125445
## 199 sq_water_faucet_filter -0.144511349 0.23371706850435
## 200 sq_water_faucet_filter -0.115764438 0.37605746039475
## 201 sq_water_charcoal_filter -0.094357710 0.39811618261251
## 202 sq_water_charcoal_filter 0.078374853 0.61705027014983
## 203 sq_water_bottled -0.374389594 0.00776080774018
## 204 sq_water_bottled -0.398004138 0.00339692534565
## 205 sq_water_none -0.097152295 0.36444330562043
## 206 sq_water_none -0.221344056 0.36842362123646
## 207 sq_water_other_type -0.118651822 0.27482381292786
## 208 sq_water_other_type -0.128580014 0.53059508137586
## 209 source 0.104342436 0.48142224323953
## 210 source -0.253170703 0.04569477728083
## 211 source 0.366718557 0.15030326977520
## 212 sex 0.047539722 0.65952777755573
## 213 race_eth_label -0.292172691 0.24558725047750
## 214 race_eth_label 0.259040647 0.45098650537994
## 215 race_eth_label 0.197393303 0.41292192888075
## 216 race_eth_label -0.004429835 0.98826938151728
## 217 race_final_label 2.990353342 0.00001177078990
## 218 race_final_label -0.750763195 0.11711937574190
## 219 race_final_label -0.213625556 0.69833970735644
## 220 race_final_label -0.201060933 0.60825038534995
## 221 race_final_label -0.528106273 0.00000214067538
## 222 race_final_label 0.144466203 0.87920452393557
## 223 race_final_label -0.541876335 0.05422224390061
## 224 race_final_label -0.641720215 0.07841348875071
## 225 ethnicity 0.052119485 0.82999524103982
## 226 ethnicity -0.004429835 0.98850968884498
## 227 rural 0.039818426 0.83089199323123
## 228 rural 0.067323679 0.68047191960674
## 229 smoking -0.258608572 0.15406242068696
## 230 smoking -0.042662611 0.71521597801004
## 231 sq_drink_alcohol 0.135862805 0.38818440115655
## 232 sq_drink_alcohol 0.048099616 0.74153340009344
## 233 sq_drink_alcohol 0.065602936 0.65217578709575
## 234 sq_average_drink_per_day 0.343582611 0.18358567488179
## 235 sq_average_drink_per_day 1.133059764 0.00724023699402
## 236 sq_average_drink_per_day 0.112919552 0.36326518486072
## 237 sq_self_hep_b -0.005245175 0.96324302106208
## 238 sq_self_hep_b -0.157051829 0.53229219046350
## 239 sq_self_hep_c 0.076666646 0.50144498984546
## 240 sq_self_hep_c 0.256164632 0.29506967023342
## 241 supp_meds_tylenol 0.142366243 0.70995004114878
## 242 supp_meds_tylenol 0.095580883 0.87917492730683
## 243 supp_meds_steroids 0.106160935 0.75379520356563
## 244 supp_meds_steroids -0.006282312 0.99525978948955
## 245 sq_water_well -0.079017038 0.48242764822963
## 246 sq_water_well -0.029840353 0.84588876056705
## 247 sq_water_tap_unfiltered 0.039163075 0.78055819320376
## 248 sq_water_tap_unfiltered 0.059596919 0.65602092727267
## 249 sq_water_house_filtration -0.064832914 0.55318487403520
## 250 sq_water_house_filtration 0.044680352 0.80820617839498
## 251 sq_water_faucet_filter 0.045347249 0.70845488436321
## 252 sq_water_faucet_filter 0.153061672 0.24212063939652
## 253 sq_water_charcoal_filter -0.087033046 0.43594263519055
## 254 sq_water_charcoal_filter -0.132945406 0.39671140672906
## 255 sq_water_bottled -0.162599041 0.25016970657883
## 256 sq_water_bottled -0.218302665 0.10999177430940
## 257 sq_water_none -0.052129747 0.62687470346805
## 258 sq_water_none 0.041283762 0.86689577335487
## 259 sq_water_other_type -0.084331471 0.43780014503922
## 260 sq_water_other_type 0.059851386 0.77047252115802
## 261 source -0.242092835 0.10550138874091
## 262 source -0.314762573 0.01381394218896
## 263 source -0.438709022 0.08785424567301
## 264 sex -0.074198882 0.49160923227275
## 265 race_eth_label -0.014642162 0.95432754792565
## 266 race_eth_label -0.023386604 0.94662591167007
## 267 race_eth_label 0.216205940 0.37788131612548
## 268 race_eth_label 0.294469251 0.33681006421286
## 269 race_final_label -0.064977808 0.92729153707793
## 270 race_final_label -0.364982301 0.47055950006093
## 271 race_final_label -0.287203346 0.62210940950523
## 272 race_final_label -0.234821601 0.57120995522041
## 273 race_final_label -0.261517532 0.02465261727798
## 274 race_final_label -0.322124510 0.74855511160560
## 275 race_final_label -0.276092067 0.35256225658707
## 276 race_final_label -0.334369593 0.38492197181345
## 277 ethnicity 0.135638436 0.57595190803645
## 278 ethnicity 0.294469251 0.33814572280042
## 279 rural 0.402235775 0.03046477151052
## 280 rural -0.025462981 0.87544973896638
## 281 smoking 0.028762081 0.87397920554473
## 282 smoking -0.132220016 0.25890118865790
## 283 sq_drink_alcohol 0.030936089 0.84399176612941
## 284 sq_drink_alcohol -0.113543959 0.43588771007607
## 285 sq_drink_alcohol 0.042489490 0.77007949378540
## 286 sq_average_drink_per_day 0.001738499 0.99459668702332
## 287 sq_average_drink_per_day 1.434035309 0.00065732225553
## 288 sq_average_drink_per_day 0.001408930 0.99089640548375
## 289 sq_self_hep_b -0.161647304 0.15490547435844
## 290 sq_self_hep_b -0.231096311 0.35688641562230
## 291 sq_self_hep_c -0.110620492 0.33226578512771
## 292 sq_self_hep_c 0.117368598 0.63123268162753
## 293 supp_meds_tylenol 0.051114225 0.89376908860934
## 294 supp_meds_tylenol -0.100001513 0.87363945925546
## 295 supp_meds_steroids 0.067647998 0.84159227516948
## 296 supp_meds_steroids -0.135451785 0.89808056927625
## 297 sq_water_well 0.028668599 0.79878534040690
## 298 sq_water_well -0.083682739 0.58576338919295
## 299 sq_water_tap_unfiltered -0.048786994 0.72768354490861
## 300 sq_water_tap_unfiltered -0.196265781 0.14160342074545
## 301 sq_water_house_filtration 0.066166986 0.54472537125353
## 302 sq_water_house_filtration 0.183741850 0.31807308447128
## 303 sq_water_faucet_filter 0.084888011 0.48456664213754
## 304 sq_water_faucet_filter 0.059173337 0.65126163583327
## 305 sq_water_charcoal_filter 0.061960093 0.57934760076695
## 306 sq_water_charcoal_filter 0.086905004 0.57974234090502
## 307 sq_water_bottled 0.001506774 0.99150577538440
## 308 sq_water_bottled -0.113192372 0.40754821004133
## 309 sq_water_none 0.033612442 0.75393115719871
## 310 sq_water_none -0.083659173 0.73415922696711
## 311 sq_water_other_type 0.024303777 0.82315529229055
## 312 sq_water_other_type -0.047333276 0.81768277140598
## 313 source -0.076284269 0.61053968426051
## 314 source 0.069320160 0.58737751970846
## 315 source -0.428567690 0.09643431102686
## 316 sex -0.125164803 0.24571033496868
## 317 race_eth_label -0.149446479 0.55373366719946
## 318 race_eth_label 0.138177043 0.68861553399695
## 319 race_eth_label 0.298831816 0.21707705491636
## 320 race_eth_label 0.014561956 0.96159120427071
## 321 race_final_label 0.233611347 0.73943226114685
## 322 race_final_label -0.682571588 0.17165331000666
## 323 race_final_label 0.067011955 0.90716829723056
## 324 race_final_label 0.012995087 0.97464817278574
## 325 race_final_label -0.430966705 0.00019045927418
## 326 race_final_label 0.542360390 0.58429976766167
## 327 race_final_label -0.300302745 0.30534074418753
## 328 race_final_label 0.025747942 0.94588993724285
## 329 ethnicity 0.156059572 0.51999177754676
## 330 ethnicity 0.014561956 0.96220473628701
## 331 rural -0.190148486 0.30702073717271
## 332 rural -0.170630017 0.29578828788987
## 333 smoking -0.409788834 0.02321464375482
## 334 smoking -0.222632007 0.05574471466387
## 335 sq_drink_alcohol -0.111496089 0.46913030842951
## 336 sq_drink_alcohol -0.046732994 0.74326861358174
## 337 sq_drink_alcohol 0.409557468 0.00422640049175
## 338 sq_average_drink_per_day -0.040424653 0.87408657104043
## 339 sq_average_drink_per_day -0.260093248 0.53096833080426
## 340 sq_average_drink_per_day -0.481474629 0.00010257804470
## 341 sq_self_hep_b -0.178851753 0.11559742974388
## 342 sq_self_hep_b 0.045228192 0.85681327359605
## 343 sq_self_hep_c -0.177866607 0.11880220685087
## 344 sq_self_hep_c 0.005156484 0.98314670068817
## 345 supp_meds_tylenol 0.443801098 0.24563690847941
## 346 supp_meds_tylenol 0.770507972 0.21985255079337
## 347 supp_meds_steroids 0.107805082 0.75003621633081
## 348 supp_meds_steroids -0.380809440 0.71869901424882
## 349 sq_water_well -0.186536945 0.09675928899761
## 350 sq_water_well -0.112239398 0.46350632851723
## 351 sq_water_tap_unfiltered -0.007174993 0.95913876245625
## 352 sq_water_tap_unfiltered 0.175492994 0.18845245498274
## 353 sq_water_house_filtration -0.160648035 0.14111864812130
## 354 sq_water_house_filtration -0.184146789 0.31607063806375
## 355 sq_water_faucet_filter -0.180991093 0.13559656249832
## 356 sq_water_faucet_filter -0.144357201 0.26925087582704
## 357 sq_water_charcoal_filter -0.171264179 0.12499948414919
## 358 sq_water_charcoal_filter -0.044223355 0.77750970889268
## 359 sq_water_bottled -0.437282541 0.00182086074127
## 360 sq_water_bottled -0.459621966 0.00069540024129
## 361 sq_water_none -0.165431763 0.12150146864344
## 362 sq_water_none -0.392843913 0.10950059241962
## 363 sq_water_other_type -0.181037121 0.09537905447064
## 364 sq_water_other_type -0.037666105 0.85393082686463
## 365 source -0.597901038 0.00005228868616
## 366 source -0.265570259 0.03370576474755
## 367 source 0.326611981 0.19403427471931
## 368 sex -0.153686852 0.15386599131094
## 369 race_eth_label 0.072315457 0.77714097293777
## 370 race_eth_label 0.474258658 0.17483025510466
## 371 race_eth_label 0.086984175 0.72246058781372
## 372 race_eth_label 0.457678603 0.13554981673754
## 373 race_final_label 0.347296961 0.62303943070383
## 374 race_final_label 1.652976248 0.00107063992872
## 375 race_final_label -0.072385667 0.90034178273734
## 376 race_final_label -0.041298065 0.92002234501068
## 377 race_final_label 0.062986709 0.58422548881022
## 378 race_final_label -0.258911280 0.79509259208138
## 379 race_final_label -0.207098635 0.48201712804661
## 380 race_final_label -0.227823677 0.55058320024782
## 381 ethnicity 0.099940992 0.67927159271312
## 382 ethnicity 0.457678603 0.13566211156982
## 383 rural -0.147231442 0.42822635251512
## 384 rural -0.260727617 0.11000532282996
## 385 smoking 0.216112013 0.23338823688727
## 386 smoking -0.066187762 0.57137978178832
## 387 sq_drink_alcohol 0.101568743 0.51822776011725
## 388 sq_drink_alcohol -0.001902538 0.98957692131135
## 389 sq_drink_alcohol 0.151438614 0.29776677122813
## 390 sq_average_drink_per_day 0.057487391 0.82519940509489
## 391 sq_average_drink_per_day -0.344924347 0.41554963544340
## 392 sq_average_drink_per_day -0.132431733 0.29051139953655
## 393 sq_self_hep_b -0.127726808 0.26138986317014
## 394 sq_self_hep_b -0.189266149 0.45099558920554
## 395 sq_self_hep_c -0.124364032 0.27562127705554
## 396 sq_self_hep_c -0.191670281 0.43308498300754
## 397 supp_meds_tylenol 0.259503283 0.49765332661943
## 398 supp_meds_tylenol 0.077719005 0.90156876480205
## 399 supp_meds_steroids 0.217854838 0.51959775927010
## 400 supp_meds_steroids -0.290150953 0.78364104048900
## 401 sq_water_well -0.035396466 0.75308976935880
## 402 sq_water_well -0.004037423 0.97903005717147
## 403 sq_water_tap_unfiltered -0.227639599 0.10460564698569
## 404 sq_water_tap_unfiltered -0.209658682 0.11625097875513
## 405 sq_water_house_filtration 0.031800030 0.76900375265055
## 406 sq_water_house_filtration 0.499637112 0.00639538693209
## 407 sq_water_faucet_filter -0.007319188 0.95193128359767
## 408 sq_water_faucet_filter 0.073005971 0.57712410414268
## 409 sq_water_charcoal_filter -0.026450287 0.81297502605541
## 410 sq_water_charcoal_filter 0.038558107 0.80596269263471
## 411 sq_water_bottled -0.128076641 0.36598943670439
## 412 sq_water_bottled -0.100027498 0.46439951786495
## 413 sq_water_none -0.001346337 0.98995994083385
## 414 sq_water_none -0.333071106 0.17591982687334
## 415 sq_water_other_type 0.029917853 0.78311655900997
## 416 sq_water_other_type 0.149328289 0.46695697882131
## 417 source -0.122335675 0.40741994614468
## 418 source 0.166651022 0.18606871857654
## 419 source -0.710857596 0.00529967763418
## 420 sex -0.081072838 0.45234435016076
## 421 race_eth_label 0.388436412 0.12719466758624
## 422 race_eth_label 0.754963047 0.03031047612560
## 423 race_eth_label 0.309387448 0.20479634375197
## 424 race_eth_label -0.069642049 0.81923914578154
## 425 race_final_label -0.542170032 0.43999328372661
## 426 race_final_label -0.494735976 0.32117620165951
## 427 race_final_label 0.319841767 0.57762843834789
## 428 race_final_label 1.390329246 0.00073512345399
## 429 race_final_label 0.081404362 0.47660773266545
## 430 race_final_label 0.533002293 0.59053852024601
## 431 race_final_label -0.225942878 0.44010936421998
## 432 race_final_label -0.560240155 0.14005245576398
## 433 ethnicity 0.353302356 0.14289994452075
## 434 ethnicity -0.069642049 0.81947623415182
## 435 rural -0.210790739 0.25793964453799
## 436 rural -0.047194429 0.77247965322541
## 437 smoking -0.559380006 0.00181874007454
## 438 smoking -0.320177642 0.00560773476027
## 439 sq_drink_alcohol -0.177862327 0.24777442714208
## 440 sq_drink_alcohol -0.166639376 0.24258750496615
## 441 sq_drink_alcohol 0.340041634 0.01719459843916
## 442 sq_average_drink_per_day 0.167092869 0.51266621906040
## 443 sq_average_drink_per_day 0.035136353 0.93254748917460
## 444 sq_average_drink_per_day -0.427661820 0.00054676111079
## 445 sq_self_hep_b -0.227855130 0.04436660508740
## 446 sq_self_hep_b 0.243875526 0.32895313970868
## 447 sq_self_hep_c -0.231106095 0.04207291408647
## 448 sq_self_hep_c 0.220640012 0.36434293822168
## 449 supp_meds_tylenol -0.324432302 0.39565811530909
## 450 supp_meds_tylenol 0.265279446 0.67233383417726
## 451 supp_meds_steroids -0.436901492 0.19622409137501
## 452 supp_meds_steroids -0.021862618 0.98346705424688
## 453 sq_water_well -0.128171207 0.25427262322351
## 454 sq_water_well -0.024950983 0.87074988658916
## 455 sq_water_tap_unfiltered -0.222055106 0.11348990546405
## 456 sq_water_tap_unfiltered -0.070017536 0.59948647097833
## 457 sq_water_house_filtration -0.132638341 0.22481218952721
## 458 sq_water_house_filtration -0.039782055 0.82866854646979
## 459 sq_water_faucet_filter -0.131448800 0.27842603953197
## 460 sq_water_faucet_filter 0.029760120 0.81982321014451
## 461 sq_water_charcoal_filter -0.114798173 0.30392858519815
## 462 sq_water_charcoal_filter 0.052864139 0.73582611255727
## 463 sq_water_bottled -0.351141936 0.01283108050418
## 464 sq_water_bottled -0.281397807 0.03857335714817
## 465 sq_water_none -0.096455729 0.36794161779746
## 466 sq_water_none -0.209125261 0.39547290349843
## 467 sq_water_other_type -0.139208496 0.20007125981871
## 468 sq_water_other_type -0.078118811 0.70304722582391
## 469 source 0.061680049 0.67844612713382
## 470 source 0.228325732 0.07252736028874
## 471 source -0.456481340 0.07474208092999
## 472 sex 0.303500510 0.00469493718684
## 473 race_eth_label 0.171448227 0.50197497862571
## 474 race_eth_label -0.082866147 0.81223970154000
## 475 race_eth_label 0.359143079 0.14277455985686
## 476 race_eth_label 0.170940815 0.57643705503039
## 477 race_final_label -0.355957031 0.61712705883957
## 478 race_final_label -0.759880711 0.13339352193114
## 479 race_final_label -0.063258723 0.91352122741302
## 480 race_final_label -0.375470252 0.36528543014309
## 481 race_final_label -0.167859238 0.14835335109304
## 482 race_final_label 1.008017086 0.31601277293424
## 483 race_final_label -0.254488284 0.39135877967639
## 484 race_final_label -0.272328943 0.47898862867844
## 485 ethnicity 0.282534341 0.24394937568137
## 486 ethnicity 0.170940815 0.57772753504936
## 487 rural -0.153308591 0.41080517756178
## 488 rural -0.052872710 0.74619014229260
## 489 smoking -0.597431528 0.00092276549008
## 490 smoking -0.121792259 0.29205421769069
## 491 sq_drink_alcohol -0.065354458 0.67660542380994
## 492 sq_drink_alcohol 0.011750535 0.93549031572168
## 493 sq_drink_alcohol 0.208068661 0.15152091369329
## 494 sq_average_drink_per_day 0.015959027 0.95098333148844
## 495 sq_average_drink_per_day 0.063766417 0.88000354530749
## 496 sq_average_drink_per_day -0.216089727 0.08417452030593
## 497 sq_self_hep_b -0.022472789 0.84347580470059
## 498 sq_self_hep_b 0.146603021 0.55989141502025
## 499 sq_self_hep_c -0.028534432 0.80272136125276
## 500 sq_self_hep_c 0.026712887 0.91313950657492
## 501 supp_meds_tylenol 0.302693492 0.42836384684072
## 502 supp_meds_tylenol 0.781438896 0.21353816457676
## 503 supp_meds_steroids -0.105591434 0.75503614022517
## 504 supp_meds_steroids -0.466254373 0.65925938965953
## 505 sq_water_well -0.067772539 0.54651262733605
## 506 sq_water_well -0.155400137 0.31136152736213
## 507 sq_water_tap_unfiltered -0.007275703 0.95872659269935
## 508 sq_water_tap_unfiltered 0.026032274 0.84574417416999
## 509 sq_water_house_filtration -0.036057060 0.74164992928475
## 510 sq_water_house_filtration -0.016420265 0.92895252444545
## 511 sq_water_faucet_filter -0.078388895 0.51835547757239
## 512 sq_water_faucet_filter -0.124962156 0.33973975540600
## 513 sq_water_charcoal_filter -0.042810377 0.70176869616977
## 514 sq_water_charcoal_filter 0.024256103 0.87718241871529
## 515 sq_water_bottled -0.343434053 0.01441692733044
## 516 sq_water_bottled -0.433131801 0.00143238137611
## 517 sq_water_none -0.039586203 0.71147423698222
## 518 sq_water_none -0.320453635 0.19292650115117
## 519 sq_water_other_type -0.044492329 0.68223041086397
## 520 sq_water_other_type -0.166568984 0.41705478475211
## 521 source 0.003415976 0.98179146397575
## 522 source 0.045020692 0.72434393181778
## 523 source -0.512003703 0.04708117779278
## 524 sex -0.023593908 0.82693773382376
## 525 race_eth_label 0.060863687 0.81187103175203
## 526 race_eth_label 0.283568579 0.41725220517750
## 527 race_eth_label 0.170801740 0.48604179615838
## 528 race_eth_label -0.239941118 0.43384263785675
## 529 race_final_label -0.462599423 0.51535911876491
## 530 race_final_label -0.486996060 0.33501616397590
## 531 race_final_label 0.006627957 0.99090900750855
## 532 race_final_label 0.762330953 0.06613992103282
## 533 race_final_label -0.107573068 0.35324429414613
## 534 race_final_label -0.151427581 0.88002788778056
## 535 race_final_label -0.076316098 0.79678214569844
## 536 race_final_label -0.540407962 0.15992207734304
## 537 ethnicity 0.142621379 0.55500838863838
## 538 ethnicity -0.239941118 0.43331808561261
## 539 rural -0.132863570 0.47604978557856
## 540 rural 0.004063870 0.98015694799679
## 541 smoking -0.339253335 0.06089981157603
## 542 smoking -0.160431500 0.16912075431888
## 543 sq_drink_alcohol 0.033696841 0.82844187593956
## 544 sq_drink_alcohol 0.039289037 0.78511449567258
## 545 sq_drink_alcohol 0.377990514 0.00887878150753
## 546 sq_average_drink_per_day -0.146498661 0.56947537328378
## 547 sq_average_drink_per_day -0.006154150 0.98827941703331
## 548 sq_average_drink_per_day -0.377444112 0.00245565678436
## 549 sq_self_hep_b -0.141051588 0.21493465638587
## 550 sq_self_hep_b 0.042106924 0.86677177612830
## 551 sq_self_hep_c -0.099793531 0.37976264387495
## 552 sq_self_hep_c 0.428406768 0.07911871955042
## 553 supp_meds_tylenol -0.241743315 0.52552310304422
## 554 supp_meds_tylenol 0.743002153 0.23522327501678
## 555 supp_meds_steroids -0.218523659 0.51797858027497
## 556 supp_meds_steroids 0.714542748 0.49868186690490
## 557 sq_water_well 0.011686060 0.91721458430984
## 558 sq_water_well 0.126086020 0.41156166219780
## 559 sq_water_tap_unfiltered -0.092200494 0.51184853848188
## 560 sq_water_tap_unfiltered -0.075435399 0.57279182355582
## 561 sq_water_house_filtration -0.011928675 0.91310090818640
## 562 sq_water_house_filtration -0.129770756 0.48093371950682
## 563 sq_water_faucet_filter -0.027285058 0.82219076468471
## 564 sq_water_faucet_filter -0.090228579 0.49076539977289
## 565 sq_water_charcoal_filter 0.036827190 0.74186846476578
## 566 sq_water_charcoal_filter 0.040403136 0.79688719547919
## 567 sq_water_bottled -0.177736099 0.20792533592252
## 568 sq_water_bottled -0.275383208 0.04357849187013
## 569 sq_water_none 0.022297708 0.83524455521135
## 570 sq_water_none -0.129187361 0.59997849432490
## 571 sq_water_other_type -0.060323288 0.57889504928244
## 572 sq_water_other_type 0.092365555 0.65262891764172
## 573 source 0.128461465 0.38687468700135
## 574 source 0.397209096 0.00181891699049
## 575 source 0.041555547 0.87053458291734
## 576 sex 0.081196520 0.45165441374254
## 577 race_eth_label -0.005861819 0.98166500714231
## 578 race_eth_label 0.072383603 0.83548591495917
## 579 race_eth_label 0.279024063 0.25418843077719
## 580 race_eth_label 0.047160447 0.87739425723636
## 581 race_final_label 1.360287326 0.05400990074290
## 582 race_final_label -0.677922478 0.17574960546630
## 583 race_final_label -0.050667294 0.92993187406726
## 584 race_final_label -0.433484494 0.29078243295854
## 585 race_final_label -0.304808265 0.00818424840147
## 586 race_final_label 0.211835891 0.83120895797992
## 587 race_final_label -0.366394274 0.21245914434555
## 588 race_final_label -0.371598373 0.32895717410029
## 589 ethnicity 0.183595676 0.44910052978292
## 590 ethnicity 0.047160447 0.87801175679233
## 591 rural -0.293698353 0.11397432465806
## 592 rural -0.225951890 0.16528201759410
## 593 smoking -0.328121339 0.07025007004598
## 594 smoking -0.086475186 0.45886310938747
## 595 sq_drink_alcohol 0.008721390 0.95557605731021
## 596 sq_drink_alcohol 0.053297528 0.71336276014425
## 597 sq_drink_alcohol 0.264186688 0.06864932704253
## 598 sq_average_drink_per_day 0.345986259 0.18167914444427
## 599 sq_average_drink_per_day -0.077065092 0.85474769829995
## 600 sq_average_drink_per_day -0.187162580 0.13320907616193
## 601 sq_self_hep_b -0.043841269 0.70010452722554
## 602 sq_self_hep_b -0.148523564 0.55475236942286
## 603 sq_self_hep_c -0.008504837 0.94062361844989
## 604 sq_self_hep_c -0.133253026 0.58632012495494
## 605 supp_meds_tylenol 0.181507556 0.63535705748588
## 606 supp_meds_tylenol 0.167413591 0.79002340503006
## 607 supp_meds_steroids 0.162567452 0.63085743718607
## 608 supp_meds_steroids -0.402999024 0.70298916766313
## 609 sq_water_well -0.031775990 0.77734871293318
## 610 sq_water_well -0.161526945 0.29269446086610
## 611 sq_water_tap_unfiltered 0.130887263 0.34954163790135
## 612 sq_water_tap_unfiltered 0.265440810 0.04667168825859
## 613 sq_water_house_filtration 0.035397161 0.74609488302110
## 614 sq_water_house_filtration 0.122866958 0.50458013295510
## 615 sq_water_faucet_filter 0.074224982 0.54112145438895
## 616 sq_water_faucet_filter 0.046001885 0.72532178061484
## 617 sq_water_charcoal_filter -0.017015905 0.87904866822130
## 618 sq_water_charcoal_filter -0.007147878 0.96368572588700
## 619 sq_water_bottled -0.303379520 0.02943808106098
## 620 sq_water_bottled -0.527135357 0.00009910794049
## 621 sq_water_none 0.032209065 0.76347440250853
## 622 sq_water_none -0.286379175 0.24456352714546
## 623 sq_water_other_type -0.000698204 0.99487761581831
## 624 sq_water_other_type 0.047675534 0.81640595739470
## 625 source 0.518825224 0.00035296753017
## 626 source 0.273293280 0.02652537691163
## 627 source 1.365624166 0.00000006218287
## 628 sex -0.152564255 0.15690319949755
## 629 race_eth_label -0.099619137 0.69832721067773
## 630 race_eth_label -0.280687329 0.42434022335422
## 631 race_eth_label -0.155025713 0.52926880834616
## 632 race_eth_label -0.240179832 0.43570815119400
## 633 race_final_label 0.363145761 0.60936886944373
## 634 race_final_label -0.125495861 0.80359047025395
## 635 race_final_label 0.085954218 0.88247237807533
## 636 race_final_label -0.557711314 0.17818156233011
## 637 race_final_label 0.057141508 0.62161421926663
## 638 race_final_label 2.154326215 0.03221506802430
## 639 race_final_label -0.132608948 0.65440808798253
## 640 race_final_label 0.322560344 0.40092858744280
## 641 ethnicity -0.143914516 0.55309406174543
## 642 ethnicity -0.240179832 0.43478718661802
## 643 rural -0.030728063 0.86877802201808
## 644 rural 0.227249815 0.16396371383201
## 645 smoking 0.383823460 0.03156290235867
## 646 smoking 0.424432242 0.00024963802757
## 647 sq_drink_alcohol -0.106510076 0.49296709044419
## 648 sq_drink_alcohol 0.296946616 0.03963369285390
## 649 sq_drink_alcohol -0.085498716 0.55172036530078
## 650 sq_average_drink_per_day -0.104427621 0.68793945814010
## 651 sq_average_drink_per_day -0.273190945 0.51845922867124
## 652 sq_average_drink_per_day 0.139826105 0.26388573205593
## 653 sq_self_hep_b 0.338679517 0.00270288931959
## 654 sq_self_hep_b -0.238220118 0.33688740607012
## 655 sq_self_hep_c 0.386303960 0.00064482875449
## 656 sq_self_hep_c 0.339735501 0.15897129077816
## 657 supp_meds_tylenol -0.039791052 0.91717244608162
## 658 supp_meds_tylenol -0.354387040 0.57295049357703
## 659 supp_meds_steroids 0.173481308 0.60813457682163
## 660 supp_meds_steroids 0.657077748 0.53424295732900
## 661 sq_water_well 0.284585889 0.01091883956148
## 662 sq_water_well -0.056920712 0.70803138909272
## 663 sq_water_tap_unfiltered 0.372682966 0.00767897644492
## 664 sq_water_tap_unfiltered 0.132440345 0.31755980881214
## 665 sq_water_house_filtration 0.276526321 0.01100469577270
## 666 sq_water_house_filtration -0.062346803 0.73243980970183
## 667 sq_water_faucet_filter 0.331222398 0.00611511609349
## 668 sq_water_faucet_filter 0.096415171 0.45709065759293
## 669 sq_water_charcoal_filter 0.289475271 0.00910866204876
## 670 sq_water_charcoal_filter -0.085752388 0.58051667684540
## 671 sq_water_bottled 0.329643039 0.01932347445562
## 672 sq_water_bottled 0.069930043 0.60590779765293
## 673 sq_water_none 0.279475921 0.00867219569147
## 674 sq_water_none -0.229269416 0.34669199788674
## 675 sq_water_other_type 0.337214768 0.00169010311525
## 676 sq_water_other_type 0.565255198 0.00524748730282
## 677 source -0.506598992 0.00049247622953
## 678 source -0.713318581 0.00000001380182
## 679 source -0.676877491 0.00655977315887
## 680 sex 0.319078694 0.00293897166878
## 681 race_eth_label -0.384561281 0.13013569457742
## 682 race_eth_label -0.015459945 0.96442794455499
## 683 race_eth_label -0.177342612 0.46598140195592
## 684 race_eth_label 0.282335829 0.35335867587746
## 685 race_final_label 1.135611694 0.10806768285307
## 686 race_final_label -0.425309066 0.39610942244147
## 687 race_final_label -0.471448517 0.41428300467105
## 688 race_final_label 0.306275468 0.45604501729654
## 689 race_final_label -0.307432538 0.00776078864512
## 690 race_final_label -0.471448517 0.63586430273092
## 691 race_final_label -0.420749965 0.15305394925531
## 692 race_final_label -0.167562266 0.66018676901763
## 693 ethnicity -0.232734365 0.33347886020211
## 694 ethnicity 0.282335829 0.35448966539951
## 695 rural -0.017570768 0.92489389560955
## 696 rural -0.109517642 0.50284536393231
## 697 smoking -0.179856476 0.32177091020604
## 698 smoking -0.072533826 0.53558707695853
## 699 sq_drink_alcohol 0.327969771 0.03594188193303
## 700 sq_drink_alcohol 0.024728206 0.86408706078347
## 701 sq_drink_alcohol -0.067189872 0.64121543008338
## 702 sq_average_drink_per_day -0.270412395 0.29799235545930
## 703 sq_average_drink_per_day 0.222094164 0.59912099850931
## 704 sq_average_drink_per_day 0.139006224 0.26608530903584
## 705 sq_self_hep_b -0.080549601 0.47899561405699
## 706 sq_self_hep_b 0.123863430 0.62208712770531
## 707 sq_self_hep_c -0.033324941 0.77046630225326
## 708 sq_self_hep_c -0.013533496 0.95592885498328
## 709 supp_meds_tylenol 0.074785778 0.84501740098413
## 710 supp_meds_tylenol -0.270640942 0.66676216096643
## 711 supp_meds_steroids 0.146786904 0.66450267863285
## 712 supp_meds_steroids -0.055286439 0.95829689341887
## 713 sq_water_well -0.071131278 0.52721094260777
## 714 sq_water_well -0.020271407 0.89496451658351
## 715 sq_water_tap_unfiltered -0.081146477 0.56352426626744
## 716 sq_water_tap_unfiltered -0.119942521 0.36983338632043
## 717 sq_water_house_filtration -0.029969350 0.78307622374691
## 718 sq_water_house_filtration 0.322655831 0.07896650237649
## 719 sq_water_faucet_filter 0.098297135 0.41138610856269
## 720 sq_water_faucet_filter 0.429929406 0.00093424336604
## 721 sq_water_charcoal_filter -0.120332622 0.27988058283521
## 722 sq_water_charcoal_filter -0.289534580 0.06445662344530
## 723 sq_water_bottled -0.103467380 0.46520637575138
## 724 sq_water_bottled -0.107528668 0.43170112368954
## 725 sq_water_none -0.018050259 0.86604968749351
## 726 sq_water_none 0.311629483 0.20541041566927
## 727 sq_water_other_type -0.069122669 0.52440830804744
## 728 sq_water_other_type -0.229725344 0.26277924482141
## Factor PFAS
## 1 sourceDUKE pf_hx_s_scld
## 2 sourceNCSU pf_hx_s_scld
## 3 sourceUNC pf_hx_s_scld
## 4 sexMale pf_hx_s_scld
## 5 race_eth_labelNHB pf_hx_s_scld
## 6 race_eth_labelNHO pf_hx_s_scld
## 7 race_eth_labelNHW pf_hx_s_scld
## 8 race_eth_labelUnknown/Not Reported pf_hx_s_scld
## 9 race_final_labelAmerican Indian pf_hx_s_scld
## 10 race_final_labelAmerican Indian/Alaskan Native pf_hx_s_scld
## 11 race_final_labelAsian pf_hx_s_scld
## 12 race_final_labelAsian/Pacific Islander pf_hx_s_scld
## 13 race_final_labelBlack pf_hx_s_scld
## 14 race_final_labelMore than one race pf_hx_s_scld
## 15 race_final_labelOther pf_hx_s_scld
## 16 race_final_labelUnknown/Not Reported pf_hx_s_scld
## 17 ethnicityNot Hispanic pf_hx_s_scld
## 18 ethnicityUnknown/Not Reported pf_hx_s_scld
## 19 ruralLiving in rural area pf_hx_s_scld
## 20 ruralUnknown/Not Reported pf_hx_s_scld
## 21 smokingSmoke or use vape pf_hx_s_scld
## 22 smokingUnknown/Not Reported pf_hx_s_scld
## 23 sq_drink_alcoholNo, former drinker (stopped) pf_hx_s_scld
## 24 sq_drink_alcoholUnknown/Not Reported pf_hx_s_scld
## 25 sq_drink_alcoholYes, current drinker pf_hx_s_scld
## 26 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_s_scld
## 27 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_s_scld
## 28 sq_average_drink_per_dayUnknown/Not Reported pf_hx_s_scld
## 29 sq_self_hep_bUnknown/Not Reported pf_hx_s_scld
## 30 sq_self_hep_bYes pf_hx_s_scld
## 31 sq_self_hep_cUnknown/Not Reported pf_hx_s_scld
## 32 sq_self_hep_cYes pf_hx_s_scld
## 33 supp_meds_tylenolUnknown/Not Reported pf_hx_s_scld
## 34 supp_meds_tylenolYes pf_hx_s_scld
## 35 supp_meds_steroidsUnknown/Not Reported pf_hx_s_scld
## 36 supp_meds_steroidsYes pf_hx_s_scld
## 37 sq_water_wellUnknown/Not Reported pf_hx_s_scld
## 38 sq_water_wellYes pf_hx_s_scld
## 39 sq_water_tap_unfilteredUnknown/Not Reported pf_hx_s_scld
## 40 sq_water_tap_unfilteredYes pf_hx_s_scld
## 41 sq_water_house_filtrationUnknown/Not Reported pf_hx_s_scld
## 42 sq_water_house_filtrationYes pf_hx_s_scld
## 43 sq_water_faucet_filterUnknown/Not Reported pf_hx_s_scld
## 44 sq_water_faucet_filterYes pf_hx_s_scld
## 45 sq_water_charcoal_filterUnknown/Not Reported pf_hx_s_scld
## 46 sq_water_charcoal_filterYes pf_hx_s_scld
## 47 sq_water_bottledUnknown/Not Reported pf_hx_s_scld
## 48 sq_water_bottledYes pf_hx_s_scld
## 49 sq_water_noneUnknown/Not Reported pf_hx_s_scld
## 50 sq_water_noneYes pf_hx_s_scld
## 51 sq_water_other_typeUnknown/Not Reported pf_hx_s_scld
## 52 sq_water_other_typeYes pf_hx_s_scld
## 53 sourceDUKE pfda_scld
## 54 sourceNCSU pfda_scld
## 55 sourceUNC pfda_scld
## 56 sexMale pfda_scld
## 57 race_eth_labelNHB pfda_scld
## 58 race_eth_labelNHO pfda_scld
## 59 race_eth_labelNHW pfda_scld
## 60 race_eth_labelUnknown/Not Reported pfda_scld
## 61 race_final_labelAmerican Indian pfda_scld
## 62 race_final_labelAmerican Indian/Alaskan Native pfda_scld
## 63 race_final_labelAsian pfda_scld
## 64 race_final_labelAsian/Pacific Islander pfda_scld
## 65 race_final_labelBlack pfda_scld
## 66 race_final_labelMore than one race pfda_scld
## 67 race_final_labelOther pfda_scld
## 68 race_final_labelUnknown/Not Reported pfda_scld
## 69 ethnicityNot Hispanic pfda_scld
## 70 ethnicityUnknown/Not Reported pfda_scld
## 71 ruralLiving in rural area pfda_scld
## 72 ruralUnknown/Not Reported pfda_scld
## 73 smokingSmoke or use vape pfda_scld
## 74 smokingUnknown/Not Reported pfda_scld
## 75 sq_drink_alcoholNo, former drinker (stopped) pfda_scld
## 76 sq_drink_alcoholUnknown/Not Reported pfda_scld
## 77 sq_drink_alcoholYes, current drinker pfda_scld
## 78 sq_average_drink_per_day1-2 alcoholic drinks per day pfda_scld
## 79 sq_average_drink_per_day3-4 alcoholic drinks per day pfda_scld
## 80 sq_average_drink_per_dayUnknown/Not Reported pfda_scld
## 81 sq_self_hep_bUnknown/Not Reported pfda_scld
## 82 sq_self_hep_bYes pfda_scld
## 83 sq_self_hep_cUnknown/Not Reported pfda_scld
## 84 sq_self_hep_cYes pfda_scld
## 85 supp_meds_tylenolUnknown/Not Reported pfda_scld
## 86 supp_meds_tylenolYes pfda_scld
## 87 supp_meds_steroidsUnknown/Not Reported pfda_scld
## 88 supp_meds_steroidsYes pfda_scld
## 89 sq_water_wellUnknown/Not Reported pfda_scld
## 90 sq_water_wellYes pfda_scld
## 91 sq_water_tap_unfilteredUnknown/Not Reported pfda_scld
## 92 sq_water_tap_unfilteredYes pfda_scld
## 93 sq_water_house_filtrationUnknown/Not Reported pfda_scld
## 94 sq_water_house_filtrationYes pfda_scld
## 95 sq_water_faucet_filterUnknown/Not Reported pfda_scld
## 96 sq_water_faucet_filterYes pfda_scld
## 97 sq_water_charcoal_filterUnknown/Not Reported pfda_scld
## 98 sq_water_charcoal_filterYes pfda_scld
## 99 sq_water_bottledUnknown/Not Reported pfda_scld
## 100 sq_water_bottledYes pfda_scld
## 101 sq_water_noneUnknown/Not Reported pfda_scld
## 102 sq_water_noneYes pfda_scld
## 103 sq_water_other_typeUnknown/Not Reported pfda_scld
## 104 sq_water_other_typeYes pfda_scld
## 105 sourceDUKE pfna_scld
## 106 sourceNCSU pfna_scld
## 107 sourceUNC pfna_scld
## 108 sexMale pfna_scld
## 109 race_eth_labelNHB pfna_scld
## 110 race_eth_labelNHO pfna_scld
## 111 race_eth_labelNHW pfna_scld
## 112 race_eth_labelUnknown/Not Reported pfna_scld
## 113 race_final_labelAmerican Indian pfna_scld
## 114 race_final_labelAmerican Indian/Alaskan Native pfna_scld
## 115 race_final_labelAsian pfna_scld
## 116 race_final_labelAsian/Pacific Islander pfna_scld
## 117 race_final_labelBlack pfna_scld
## 118 race_final_labelMore than one race pfna_scld
## 119 race_final_labelOther pfna_scld
## 120 race_final_labelUnknown/Not Reported pfna_scld
## 121 ethnicityNot Hispanic pfna_scld
## 122 ethnicityUnknown/Not Reported pfna_scld
## 123 ruralLiving in rural area pfna_scld
## 124 ruralUnknown/Not Reported pfna_scld
## 125 smokingSmoke or use vape pfna_scld
## 126 smokingUnknown/Not Reported pfna_scld
## 127 sq_drink_alcoholNo, former drinker (stopped) pfna_scld
## 128 sq_drink_alcoholUnknown/Not Reported pfna_scld
## 129 sq_drink_alcoholYes, current drinker pfna_scld
## 130 sq_average_drink_per_day1-2 alcoholic drinks per day pfna_scld
## 131 sq_average_drink_per_day3-4 alcoholic drinks per day pfna_scld
## 132 sq_average_drink_per_dayUnknown/Not Reported pfna_scld
## 133 sq_self_hep_bUnknown/Not Reported pfna_scld
## 134 sq_self_hep_bYes pfna_scld
## 135 sq_self_hep_cUnknown/Not Reported pfna_scld
## 136 sq_self_hep_cYes pfna_scld
## 137 supp_meds_tylenolUnknown/Not Reported pfna_scld
## 138 supp_meds_tylenolYes pfna_scld
## 139 supp_meds_steroidsUnknown/Not Reported pfna_scld
## 140 supp_meds_steroidsYes pfna_scld
## 141 sq_water_wellUnknown/Not Reported pfna_scld
## 142 sq_water_wellYes pfna_scld
## 143 sq_water_tap_unfilteredUnknown/Not Reported pfna_scld
## 144 sq_water_tap_unfilteredYes pfna_scld
## 145 sq_water_house_filtrationUnknown/Not Reported pfna_scld
## 146 sq_water_house_filtrationYes pfna_scld
## 147 sq_water_faucet_filterUnknown/Not Reported pfna_scld
## 148 sq_water_faucet_filterYes pfna_scld
## 149 sq_water_charcoal_filterUnknown/Not Reported pfna_scld
## 150 sq_water_charcoal_filterYes pfna_scld
## 151 sq_water_bottledUnknown/Not Reported pfna_scld
## 152 sq_water_bottledYes pfna_scld
## 153 sq_water_noneUnknown/Not Reported pfna_scld
## 154 sq_water_noneYes pfna_scld
## 155 sq_water_other_typeUnknown/Not Reported pfna_scld
## 156 sq_water_other_typeYes pfna_scld
## 157 sourceDUKE pfos_scld
## 158 sourceNCSU pfos_scld
## 159 sourceUNC pfos_scld
## 160 sexMale pfos_scld
## 161 race_eth_labelNHB pfos_scld
## 162 race_eth_labelNHO pfos_scld
## 163 race_eth_labelNHW pfos_scld
## 164 race_eth_labelUnknown/Not Reported pfos_scld
## 165 race_final_labelAmerican Indian pfos_scld
## 166 race_final_labelAmerican Indian/Alaskan Native pfos_scld
## 167 race_final_labelAsian pfos_scld
## 168 race_final_labelAsian/Pacific Islander pfos_scld
## 169 race_final_labelBlack pfos_scld
## 170 race_final_labelMore than one race pfos_scld
## 171 race_final_labelOther pfos_scld
## 172 race_final_labelUnknown/Not Reported pfos_scld
## 173 ethnicityNot Hispanic pfos_scld
## 174 ethnicityUnknown/Not Reported pfos_scld
## 175 ruralLiving in rural area pfos_scld
## 176 ruralUnknown/Not Reported pfos_scld
## 177 smokingSmoke or use vape pfos_scld
## 178 smokingUnknown/Not Reported pfos_scld
## 179 sq_drink_alcoholNo, former drinker (stopped) pfos_scld
## 180 sq_drink_alcoholUnknown/Not Reported pfos_scld
## 181 sq_drink_alcoholYes, current drinker pfos_scld
## 182 sq_average_drink_per_day1-2 alcoholic drinks per day pfos_scld
## 183 sq_average_drink_per_day3-4 alcoholic drinks per day pfos_scld
## 184 sq_average_drink_per_dayUnknown/Not Reported pfos_scld
## 185 sq_self_hep_bUnknown/Not Reported pfos_scld
## 186 sq_self_hep_bYes pfos_scld
## 187 sq_self_hep_cUnknown/Not Reported pfos_scld
## 188 sq_self_hep_cYes pfos_scld
## 189 supp_meds_tylenolUnknown/Not Reported pfos_scld
## 190 supp_meds_tylenolYes pfos_scld
## 191 supp_meds_steroidsUnknown/Not Reported pfos_scld
## 192 supp_meds_steroidsYes pfos_scld
## 193 sq_water_wellUnknown/Not Reported pfos_scld
## 194 sq_water_wellYes pfos_scld
## 195 sq_water_tap_unfilteredUnknown/Not Reported pfos_scld
## 196 sq_water_tap_unfilteredYes pfos_scld
## 197 sq_water_house_filtrationUnknown/Not Reported pfos_scld
## 198 sq_water_house_filtrationYes pfos_scld
## 199 sq_water_faucet_filterUnknown/Not Reported pfos_scld
## 200 sq_water_faucet_filterYes pfos_scld
## 201 sq_water_charcoal_filterUnknown/Not Reported pfos_scld
## 202 sq_water_charcoal_filterYes pfos_scld
## 203 sq_water_bottledUnknown/Not Reported pfos_scld
## 204 sq_water_bottledYes pfos_scld
## 205 sq_water_noneUnknown/Not Reported pfos_scld
## 206 sq_water_noneYes pfos_scld
## 207 sq_water_other_typeUnknown/Not Reported pfos_scld
## 208 sq_water_other_typeYes pfos_scld
## 209 sourceDUKE pf_hp_a_scld
## 210 sourceNCSU pf_hp_a_scld
## 211 sourceUNC pf_hp_a_scld
## 212 sexMale pf_hp_a_scld
## 213 race_eth_labelNHB pf_hp_a_scld
## 214 race_eth_labelNHO pf_hp_a_scld
## 215 race_eth_labelNHW pf_hp_a_scld
## 216 race_eth_labelUnknown/Not Reported pf_hp_a_scld
## 217 race_final_labelAmerican Indian pf_hp_a_scld
## 218 race_final_labelAmerican Indian/Alaskan Native pf_hp_a_scld
## 219 race_final_labelAsian pf_hp_a_scld
## 220 race_final_labelAsian/Pacific Islander pf_hp_a_scld
## 221 race_final_labelBlack pf_hp_a_scld
## 222 race_final_labelMore than one race pf_hp_a_scld
## 223 race_final_labelOther pf_hp_a_scld
## 224 race_final_labelUnknown/Not Reported pf_hp_a_scld
## 225 ethnicityNot Hispanic pf_hp_a_scld
## 226 ethnicityUnknown/Not Reported pf_hp_a_scld
## 227 ruralLiving in rural area pf_hp_a_scld
## 228 ruralUnknown/Not Reported pf_hp_a_scld
## 229 smokingSmoke or use vape pf_hp_a_scld
## 230 smokingUnknown/Not Reported pf_hp_a_scld
## 231 sq_drink_alcoholNo, former drinker (stopped) pf_hp_a_scld
## 232 sq_drink_alcoholUnknown/Not Reported pf_hp_a_scld
## 233 sq_drink_alcoholYes, current drinker pf_hp_a_scld
## 234 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_a_scld
## 235 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_a_scld
## 236 sq_average_drink_per_dayUnknown/Not Reported pf_hp_a_scld
## 237 sq_self_hep_bUnknown/Not Reported pf_hp_a_scld
## 238 sq_self_hep_bYes pf_hp_a_scld
## 239 sq_self_hep_cUnknown/Not Reported pf_hp_a_scld
## 240 sq_self_hep_cYes pf_hp_a_scld
## 241 supp_meds_tylenolUnknown/Not Reported pf_hp_a_scld
## 242 supp_meds_tylenolYes pf_hp_a_scld
## 243 supp_meds_steroidsUnknown/Not Reported pf_hp_a_scld
## 244 supp_meds_steroidsYes pf_hp_a_scld
## 245 sq_water_wellUnknown/Not Reported pf_hp_a_scld
## 246 sq_water_wellYes pf_hp_a_scld
## 247 sq_water_tap_unfilteredUnknown/Not Reported pf_hp_a_scld
## 248 sq_water_tap_unfilteredYes pf_hp_a_scld
## 249 sq_water_house_filtrationUnknown/Not Reported pf_hp_a_scld
## 250 sq_water_house_filtrationYes pf_hp_a_scld
## 251 sq_water_faucet_filterUnknown/Not Reported pf_hp_a_scld
## 252 sq_water_faucet_filterYes pf_hp_a_scld
## 253 sq_water_charcoal_filterUnknown/Not Reported pf_hp_a_scld
## 254 sq_water_charcoal_filterYes pf_hp_a_scld
## 255 sq_water_bottledUnknown/Not Reported pf_hp_a_scld
## 256 sq_water_bottledYes pf_hp_a_scld
## 257 sq_water_noneUnknown/Not Reported pf_hp_a_scld
## 258 sq_water_noneYes pf_hp_a_scld
## 259 sq_water_other_typeUnknown/Not Reported pf_hp_a_scld
## 260 sq_water_other_typeYes pf_hp_a_scld
## 261 sourceDUKE pfbs_scld
## 262 sourceNCSU pfbs_scld
## 263 sourceUNC pfbs_scld
## 264 sexMale pfbs_scld
## 265 race_eth_labelNHB pfbs_scld
## 266 race_eth_labelNHO pfbs_scld
## 267 race_eth_labelNHW pfbs_scld
## 268 race_eth_labelUnknown/Not Reported pfbs_scld
## 269 race_final_labelAmerican Indian pfbs_scld
## 270 race_final_labelAmerican Indian/Alaskan Native pfbs_scld
## 271 race_final_labelAsian pfbs_scld
## 272 race_final_labelAsian/Pacific Islander pfbs_scld
## 273 race_final_labelBlack pfbs_scld
## 274 race_final_labelMore than one race pfbs_scld
## 275 race_final_labelOther pfbs_scld
## 276 race_final_labelUnknown/Not Reported pfbs_scld
## 277 ethnicityNot Hispanic pfbs_scld
## 278 ethnicityUnknown/Not Reported pfbs_scld
## 279 ruralLiving in rural area pfbs_scld
## 280 ruralUnknown/Not Reported pfbs_scld
## 281 smokingSmoke or use vape pfbs_scld
## 282 smokingUnknown/Not Reported pfbs_scld
## 283 sq_drink_alcoholNo, former drinker (stopped) pfbs_scld
## 284 sq_drink_alcoholUnknown/Not Reported pfbs_scld
## 285 sq_drink_alcoholYes, current drinker pfbs_scld
## 286 sq_average_drink_per_day1-2 alcoholic drinks per day pfbs_scld
## 287 sq_average_drink_per_day3-4 alcoholic drinks per day pfbs_scld
## 288 sq_average_drink_per_dayUnknown/Not Reported pfbs_scld
## 289 sq_self_hep_bUnknown/Not Reported pfbs_scld
## 290 sq_self_hep_bYes pfbs_scld
## 291 sq_self_hep_cUnknown/Not Reported pfbs_scld
## 292 sq_self_hep_cYes pfbs_scld
## 293 supp_meds_tylenolUnknown/Not Reported pfbs_scld
## 294 supp_meds_tylenolYes pfbs_scld
## 295 supp_meds_steroidsUnknown/Not Reported pfbs_scld
## 296 supp_meds_steroidsYes pfbs_scld
## 297 sq_water_wellUnknown/Not Reported pfbs_scld
## 298 sq_water_wellYes pfbs_scld
## 299 sq_water_tap_unfilteredUnknown/Not Reported pfbs_scld
## 300 sq_water_tap_unfilteredYes pfbs_scld
## 301 sq_water_house_filtrationUnknown/Not Reported pfbs_scld
## 302 sq_water_house_filtrationYes pfbs_scld
## 303 sq_water_faucet_filterUnknown/Not Reported pfbs_scld
## 304 sq_water_faucet_filterYes pfbs_scld
## 305 sq_water_charcoal_filterUnknown/Not Reported pfbs_scld
## 306 sq_water_charcoal_filterYes pfbs_scld
## 307 sq_water_bottledUnknown/Not Reported pfbs_scld
## 308 sq_water_bottledYes pfbs_scld
## 309 sq_water_noneUnknown/Not Reported pfbs_scld
## 310 sq_water_noneYes pfbs_scld
## 311 sq_water_other_typeUnknown/Not Reported pfbs_scld
## 312 sq_water_other_typeYes pfbs_scld
## 313 sourceDUKE pfoa_scld
## 314 sourceNCSU pfoa_scld
## 315 sourceUNC pfoa_scld
## 316 sexMale pfoa_scld
## 317 race_eth_labelNHB pfoa_scld
## 318 race_eth_labelNHO pfoa_scld
## 319 race_eth_labelNHW pfoa_scld
## 320 race_eth_labelUnknown/Not Reported pfoa_scld
## 321 race_final_labelAmerican Indian pfoa_scld
## 322 race_final_labelAmerican Indian/Alaskan Native pfoa_scld
## 323 race_final_labelAsian pfoa_scld
## 324 race_final_labelAsian/Pacific Islander pfoa_scld
## 325 race_final_labelBlack pfoa_scld
## 326 race_final_labelMore than one race pfoa_scld
## 327 race_final_labelOther pfoa_scld
## 328 race_final_labelUnknown/Not Reported pfoa_scld
## 329 ethnicityNot Hispanic pfoa_scld
## 330 ethnicityUnknown/Not Reported pfoa_scld
## 331 ruralLiving in rural area pfoa_scld
## 332 ruralUnknown/Not Reported pfoa_scld
## 333 smokingSmoke or use vape pfoa_scld
## 334 smokingUnknown/Not Reported pfoa_scld
## 335 sq_drink_alcoholNo, former drinker (stopped) pfoa_scld
## 336 sq_drink_alcoholUnknown/Not Reported pfoa_scld
## 337 sq_drink_alcoholYes, current drinker pfoa_scld
## 338 sq_average_drink_per_day1-2 alcoholic drinks per day pfoa_scld
## 339 sq_average_drink_per_day3-4 alcoholic drinks per day pfoa_scld
## 340 sq_average_drink_per_dayUnknown/Not Reported pfoa_scld
## 341 sq_self_hep_bUnknown/Not Reported pfoa_scld
## 342 sq_self_hep_bYes pfoa_scld
## 343 sq_self_hep_cUnknown/Not Reported pfoa_scld
## 344 sq_self_hep_cYes pfoa_scld
## 345 supp_meds_tylenolUnknown/Not Reported pfoa_scld
## 346 supp_meds_tylenolYes pfoa_scld
## 347 supp_meds_steroidsUnknown/Not Reported pfoa_scld
## 348 supp_meds_steroidsYes pfoa_scld
## 349 sq_water_wellUnknown/Not Reported pfoa_scld
## 350 sq_water_wellYes pfoa_scld
## 351 sq_water_tap_unfilteredUnknown/Not Reported pfoa_scld
## 352 sq_water_tap_unfilteredYes pfoa_scld
## 353 sq_water_house_filtrationUnknown/Not Reported pfoa_scld
## 354 sq_water_house_filtrationYes pfoa_scld
## 355 sq_water_faucet_filterUnknown/Not Reported pfoa_scld
## 356 sq_water_faucet_filterYes pfoa_scld
## 357 sq_water_charcoal_filterUnknown/Not Reported pfoa_scld
## 358 sq_water_charcoal_filterYes pfoa_scld
## 359 sq_water_bottledUnknown/Not Reported pfoa_scld
## 360 sq_water_bottledYes pfoa_scld
## 361 sq_water_noneUnknown/Not Reported pfoa_scld
## 362 sq_water_noneYes pfoa_scld
## 363 sq_water_other_typeUnknown/Not Reported pfoa_scld
## 364 sq_water_other_typeYes pfoa_scld
## 365 sourceDUKE pf_pe_a_scld
## 366 sourceNCSU pf_pe_a_scld
## 367 sourceUNC pf_pe_a_scld
## 368 sexMale pf_pe_a_scld
## 369 race_eth_labelNHB pf_pe_a_scld
## 370 race_eth_labelNHO pf_pe_a_scld
## 371 race_eth_labelNHW pf_pe_a_scld
## 372 race_eth_labelUnknown/Not Reported pf_pe_a_scld
## 373 race_final_labelAmerican Indian pf_pe_a_scld
## 374 race_final_labelAmerican Indian/Alaskan Native pf_pe_a_scld
## 375 race_final_labelAsian pf_pe_a_scld
## 376 race_final_labelAsian/Pacific Islander pf_pe_a_scld
## 377 race_final_labelBlack pf_pe_a_scld
## 378 race_final_labelMore than one race pf_pe_a_scld
## 379 race_final_labelOther pf_pe_a_scld
## 380 race_final_labelUnknown/Not Reported pf_pe_a_scld
## 381 ethnicityNot Hispanic pf_pe_a_scld
## 382 ethnicityUnknown/Not Reported pf_pe_a_scld
## 383 ruralLiving in rural area pf_pe_a_scld
## 384 ruralUnknown/Not Reported pf_pe_a_scld
## 385 smokingSmoke or use vape pf_pe_a_scld
## 386 smokingUnknown/Not Reported pf_pe_a_scld
## 387 sq_drink_alcoholNo, former drinker (stopped) pf_pe_a_scld
## 388 sq_drink_alcoholUnknown/Not Reported pf_pe_a_scld
## 389 sq_drink_alcoholYes, current drinker pf_pe_a_scld
## 390 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_a_scld
## 391 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_a_scld
## 392 sq_average_drink_per_dayUnknown/Not Reported pf_pe_a_scld
## 393 sq_self_hep_bUnknown/Not Reported pf_pe_a_scld
## 394 sq_self_hep_bYes pf_pe_a_scld
## 395 sq_self_hep_cUnknown/Not Reported pf_pe_a_scld
## 396 sq_self_hep_cYes pf_pe_a_scld
## 397 supp_meds_tylenolUnknown/Not Reported pf_pe_a_scld
## 398 supp_meds_tylenolYes pf_pe_a_scld
## 399 supp_meds_steroidsUnknown/Not Reported pf_pe_a_scld
## 400 supp_meds_steroidsYes pf_pe_a_scld
## 401 sq_water_wellUnknown/Not Reported pf_pe_a_scld
## 402 sq_water_wellYes pf_pe_a_scld
## 403 sq_water_tap_unfilteredUnknown/Not Reported pf_pe_a_scld
## 404 sq_water_tap_unfilteredYes pf_pe_a_scld
## 405 sq_water_house_filtrationUnknown/Not Reported pf_pe_a_scld
## 406 sq_water_house_filtrationYes pf_pe_a_scld
## 407 sq_water_faucet_filterUnknown/Not Reported pf_pe_a_scld
## 408 sq_water_faucet_filterYes pf_pe_a_scld
## 409 sq_water_charcoal_filterUnknown/Not Reported pf_pe_a_scld
## 410 sq_water_charcoal_filterYes pf_pe_a_scld
## 411 sq_water_bottledUnknown/Not Reported pf_pe_a_scld
## 412 sq_water_bottledYes pf_pe_a_scld
## 413 sq_water_noneUnknown/Not Reported pf_pe_a_scld
## 414 sq_water_noneYes pf_pe_a_scld
## 415 sq_water_other_typeUnknown/Not Reported pf_pe_a_scld
## 416 sq_water_other_typeYes pf_pe_a_scld
## 417 sourceDUKE pf_un_a_scld
## 418 sourceNCSU pf_un_a_scld
## 419 sourceUNC pf_un_a_scld
## 420 sexMale pf_un_a_scld
## 421 race_eth_labelNHB pf_un_a_scld
## 422 race_eth_labelNHO pf_un_a_scld
## 423 race_eth_labelNHW pf_un_a_scld
## 424 race_eth_labelUnknown/Not Reported pf_un_a_scld
## 425 race_final_labelAmerican Indian pf_un_a_scld
## 426 race_final_labelAmerican Indian/Alaskan Native pf_un_a_scld
## 427 race_final_labelAsian pf_un_a_scld
## 428 race_final_labelAsian/Pacific Islander pf_un_a_scld
## 429 race_final_labelBlack pf_un_a_scld
## 430 race_final_labelMore than one race pf_un_a_scld
## 431 race_final_labelOther pf_un_a_scld
## 432 race_final_labelUnknown/Not Reported pf_un_a_scld
## 433 ethnicityNot Hispanic pf_un_a_scld
## 434 ethnicityUnknown/Not Reported pf_un_a_scld
## 435 ruralLiving in rural area pf_un_a_scld
## 436 ruralUnknown/Not Reported pf_un_a_scld
## 437 smokingSmoke or use vape pf_un_a_scld
## 438 smokingUnknown/Not Reported pf_un_a_scld
## 439 sq_drink_alcoholNo, former drinker (stopped) pf_un_a_scld
## 440 sq_drink_alcoholUnknown/Not Reported pf_un_a_scld
## 441 sq_drink_alcoholYes, current drinker pf_un_a_scld
## 442 sq_average_drink_per_day1-2 alcoholic drinks per day pf_un_a_scld
## 443 sq_average_drink_per_day3-4 alcoholic drinks per day pf_un_a_scld
## 444 sq_average_drink_per_dayUnknown/Not Reported pf_un_a_scld
## 445 sq_self_hep_bUnknown/Not Reported pf_un_a_scld
## 446 sq_self_hep_bYes pf_un_a_scld
## 447 sq_self_hep_cUnknown/Not Reported pf_un_a_scld
## 448 sq_self_hep_cYes pf_un_a_scld
## 449 supp_meds_tylenolUnknown/Not Reported pf_un_a_scld
## 450 supp_meds_tylenolYes pf_un_a_scld
## 451 supp_meds_steroidsUnknown/Not Reported pf_un_a_scld
## 452 supp_meds_steroidsYes pf_un_a_scld
## 453 sq_water_wellUnknown/Not Reported pf_un_a_scld
## 454 sq_water_wellYes pf_un_a_scld
## 455 sq_water_tap_unfilteredUnknown/Not Reported pf_un_a_scld
## 456 sq_water_tap_unfilteredYes pf_un_a_scld
## 457 sq_water_house_filtrationUnknown/Not Reported pf_un_a_scld
## 458 sq_water_house_filtrationYes pf_un_a_scld
## 459 sq_water_faucet_filterUnknown/Not Reported pf_un_a_scld
## 460 sq_water_faucet_filterYes pf_un_a_scld
## 461 sq_water_charcoal_filterUnknown/Not Reported pf_un_a_scld
## 462 sq_water_charcoal_filterYes pf_un_a_scld
## 463 sq_water_bottledUnknown/Not Reported pf_un_a_scld
## 464 sq_water_bottledYes pf_un_a_scld
## 465 sq_water_noneUnknown/Not Reported pf_un_a_scld
## 466 sq_water_noneYes pf_un_a_scld
## 467 sq_water_other_typeUnknown/Not Reported pf_un_a_scld
## 468 sq_water_other_typeYes pf_un_a_scld
## 469 sourceDUKE pf_hp_s_scld
## 470 sourceNCSU pf_hp_s_scld
## 471 sourceUNC pf_hp_s_scld
## 472 sexMale pf_hp_s_scld
## 473 race_eth_labelNHB pf_hp_s_scld
## 474 race_eth_labelNHO pf_hp_s_scld
## 475 race_eth_labelNHW pf_hp_s_scld
## 476 race_eth_labelUnknown/Not Reported pf_hp_s_scld
## 477 race_final_labelAmerican Indian pf_hp_s_scld
## 478 race_final_labelAmerican Indian/Alaskan Native pf_hp_s_scld
## 479 race_final_labelAsian pf_hp_s_scld
## 480 race_final_labelAsian/Pacific Islander pf_hp_s_scld
## 481 race_final_labelBlack pf_hp_s_scld
## 482 race_final_labelMore than one race pf_hp_s_scld
## 483 race_final_labelOther pf_hp_s_scld
## 484 race_final_labelUnknown/Not Reported pf_hp_s_scld
## 485 ethnicityNot Hispanic pf_hp_s_scld
## 486 ethnicityUnknown/Not Reported pf_hp_s_scld
## 487 ruralLiving in rural area pf_hp_s_scld
## 488 ruralUnknown/Not Reported pf_hp_s_scld
## 489 smokingSmoke or use vape pf_hp_s_scld
## 490 smokingUnknown/Not Reported pf_hp_s_scld
## 491 sq_drink_alcoholNo, former drinker (stopped) pf_hp_s_scld
## 492 sq_drink_alcoholUnknown/Not Reported pf_hp_s_scld
## 493 sq_drink_alcoholYes, current drinker pf_hp_s_scld
## 494 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_s_scld
## 495 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_s_scld
## 496 sq_average_drink_per_dayUnknown/Not Reported pf_hp_s_scld
## 497 sq_self_hep_bUnknown/Not Reported pf_hp_s_scld
## 498 sq_self_hep_bYes pf_hp_s_scld
## 499 sq_self_hep_cUnknown/Not Reported pf_hp_s_scld
## 500 sq_self_hep_cYes pf_hp_s_scld
## 501 supp_meds_tylenolUnknown/Not Reported pf_hp_s_scld
## 502 supp_meds_tylenolYes pf_hp_s_scld
## 503 supp_meds_steroidsUnknown/Not Reported pf_hp_s_scld
## 504 supp_meds_steroidsYes pf_hp_s_scld
## 505 sq_water_wellUnknown/Not Reported pf_hp_s_scld
## 506 sq_water_wellYes pf_hp_s_scld
## 507 sq_water_tap_unfilteredUnknown/Not Reported pf_hp_s_scld
## 508 sq_water_tap_unfilteredYes pf_hp_s_scld
## 509 sq_water_house_filtrationUnknown/Not Reported pf_hp_s_scld
## 510 sq_water_house_filtrationYes pf_hp_s_scld
## 511 sq_water_faucet_filterUnknown/Not Reported pf_hp_s_scld
## 512 sq_water_faucet_filterYes pf_hp_s_scld
## 513 sq_water_charcoal_filterUnknown/Not Reported pf_hp_s_scld
## 514 sq_water_charcoal_filterYes pf_hp_s_scld
## 515 sq_water_bottledUnknown/Not Reported pf_hp_s_scld
## 516 sq_water_bottledYes pf_hp_s_scld
## 517 sq_water_noneUnknown/Not Reported pf_hp_s_scld
## 518 sq_water_noneYes pf_hp_s_scld
## 519 sq_water_other_typeUnknown/Not Reported pf_hp_s_scld
## 520 sq_water_other_typeYes pf_hp_s_scld
## 521 sourceDUKE pf_do_a_scld
## 522 sourceNCSU pf_do_a_scld
## 523 sourceUNC pf_do_a_scld
## 524 sexMale pf_do_a_scld
## 525 race_eth_labelNHB pf_do_a_scld
## 526 race_eth_labelNHO pf_do_a_scld
## 527 race_eth_labelNHW pf_do_a_scld
## 528 race_eth_labelUnknown/Not Reported pf_do_a_scld
## 529 race_final_labelAmerican Indian pf_do_a_scld
## 530 race_final_labelAmerican Indian/Alaskan Native pf_do_a_scld
## 531 race_final_labelAsian pf_do_a_scld
## 532 race_final_labelAsian/Pacific Islander pf_do_a_scld
## 533 race_final_labelBlack pf_do_a_scld
## 534 race_final_labelMore than one race pf_do_a_scld
## 535 race_final_labelOther pf_do_a_scld
## 536 race_final_labelUnknown/Not Reported pf_do_a_scld
## 537 ethnicityNot Hispanic pf_do_a_scld
## 538 ethnicityUnknown/Not Reported pf_do_a_scld
## 539 ruralLiving in rural area pf_do_a_scld
## 540 ruralUnknown/Not Reported pf_do_a_scld
## 541 smokingSmoke or use vape pf_do_a_scld
## 542 smokingUnknown/Not Reported pf_do_a_scld
## 543 sq_drink_alcoholNo, former drinker (stopped) pf_do_a_scld
## 544 sq_drink_alcoholUnknown/Not Reported pf_do_a_scld
## 545 sq_drink_alcoholYes, current drinker pf_do_a_scld
## 546 sq_average_drink_per_day1-2 alcoholic drinks per day pf_do_a_scld
## 547 sq_average_drink_per_day3-4 alcoholic drinks per day pf_do_a_scld
## 548 sq_average_drink_per_dayUnknown/Not Reported pf_do_a_scld
## 549 sq_self_hep_bUnknown/Not Reported pf_do_a_scld
## 550 sq_self_hep_bYes pf_do_a_scld
## 551 sq_self_hep_cUnknown/Not Reported pf_do_a_scld
## 552 sq_self_hep_cYes pf_do_a_scld
## 553 supp_meds_tylenolUnknown/Not Reported pf_do_a_scld
## 554 supp_meds_tylenolYes pf_do_a_scld
## 555 supp_meds_steroidsUnknown/Not Reported pf_do_a_scld
## 556 supp_meds_steroidsYes pf_do_a_scld
## 557 sq_water_wellUnknown/Not Reported pf_do_a_scld
## 558 sq_water_wellYes pf_do_a_scld
## 559 sq_water_tap_unfilteredUnknown/Not Reported pf_do_a_scld
## 560 sq_water_tap_unfilteredYes pf_do_a_scld
## 561 sq_water_house_filtrationUnknown/Not Reported pf_do_a_scld
## 562 sq_water_house_filtrationYes pf_do_a_scld
## 563 sq_water_faucet_filterUnknown/Not Reported pf_do_a_scld
## 564 sq_water_faucet_filterYes pf_do_a_scld
## 565 sq_water_charcoal_filterUnknown/Not Reported pf_do_a_scld
## 566 sq_water_charcoal_filterYes pf_do_a_scld
## 567 sq_water_bottledUnknown/Not Reported pf_do_a_scld
## 568 sq_water_bottledYes pf_do_a_scld
## 569 sq_water_noneUnknown/Not Reported pf_do_a_scld
## 570 sq_water_noneYes pf_do_a_scld
## 571 sq_water_other_typeUnknown/Not Reported pf_do_a_scld
## 572 sq_water_other_typeYes pf_do_a_scld
## 573 sourceDUKE pf_pe_s_scld
## 574 sourceNCSU pf_pe_s_scld
## 575 sourceUNC pf_pe_s_scld
## 576 sexMale pf_pe_s_scld
## 577 race_eth_labelNHB pf_pe_s_scld
## 578 race_eth_labelNHO pf_pe_s_scld
## 579 race_eth_labelNHW pf_pe_s_scld
## 580 race_eth_labelUnknown/Not Reported pf_pe_s_scld
## 581 race_final_labelAmerican Indian pf_pe_s_scld
## 582 race_final_labelAmerican Indian/Alaskan Native pf_pe_s_scld
## 583 race_final_labelAsian pf_pe_s_scld
## 584 race_final_labelAsian/Pacific Islander pf_pe_s_scld
## 585 race_final_labelBlack pf_pe_s_scld
## 586 race_final_labelMore than one race pf_pe_s_scld
## 587 race_final_labelOther pf_pe_s_scld
## 588 race_final_labelUnknown/Not Reported pf_pe_s_scld
## 589 ethnicityNot Hispanic pf_pe_s_scld
## 590 ethnicityUnknown/Not Reported pf_pe_s_scld
## 591 ruralLiving in rural area pf_pe_s_scld
## 592 ruralUnknown/Not Reported pf_pe_s_scld
## 593 smokingSmoke or use vape pf_pe_s_scld
## 594 smokingUnknown/Not Reported pf_pe_s_scld
## 595 sq_drink_alcoholNo, former drinker (stopped) pf_pe_s_scld
## 596 sq_drink_alcoholUnknown/Not Reported pf_pe_s_scld
## 597 sq_drink_alcoholYes, current drinker pf_pe_s_scld
## 598 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_s_scld
## 599 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_s_scld
## 600 sq_average_drink_per_dayUnknown/Not Reported pf_pe_s_scld
## 601 sq_self_hep_bUnknown/Not Reported pf_pe_s_scld
## 602 sq_self_hep_bYes pf_pe_s_scld
## 603 sq_self_hep_cUnknown/Not Reported pf_pe_s_scld
## 604 sq_self_hep_cYes pf_pe_s_scld
## 605 supp_meds_tylenolUnknown/Not Reported pf_pe_s_scld
## 606 supp_meds_tylenolYes pf_pe_s_scld
## 607 supp_meds_steroidsUnknown/Not Reported pf_pe_s_scld
## 608 supp_meds_steroidsYes pf_pe_s_scld
## 609 sq_water_wellUnknown/Not Reported pf_pe_s_scld
## 610 sq_water_wellYes pf_pe_s_scld
## 611 sq_water_tap_unfilteredUnknown/Not Reported pf_pe_s_scld
## 612 sq_water_tap_unfilteredYes pf_pe_s_scld
## 613 sq_water_house_filtrationUnknown/Not Reported pf_pe_s_scld
## 614 sq_water_house_filtrationYes pf_pe_s_scld
## 615 sq_water_faucet_filterUnknown/Not Reported pf_pe_s_scld
## 616 sq_water_faucet_filterYes pf_pe_s_scld
## 617 sq_water_charcoal_filterUnknown/Not Reported pf_pe_s_scld
## 618 sq_water_charcoal_filterYes pf_pe_s_scld
## 619 sq_water_bottledUnknown/Not Reported pf_pe_s_scld
## 620 sq_water_bottledYes pf_pe_s_scld
## 621 sq_water_noneUnknown/Not Reported pf_pe_s_scld
## 622 sq_water_noneYes pf_pe_s_scld
## 623 sq_water_other_typeUnknown/Not Reported pf_pe_s_scld
## 624 sq_water_other_typeYes pf_pe_s_scld
## 625 sourceDUKE pf_hx_a_scld
## 626 sourceNCSU pf_hx_a_scld
## 627 sourceUNC pf_hx_a_scld
## 628 sexMale pf_hx_a_scld
## 629 race_eth_labelNHB pf_hx_a_scld
## 630 race_eth_labelNHO pf_hx_a_scld
## 631 race_eth_labelNHW pf_hx_a_scld
## 632 race_eth_labelUnknown/Not Reported pf_hx_a_scld
## 633 race_final_labelAmerican Indian pf_hx_a_scld
## 634 race_final_labelAmerican Indian/Alaskan Native pf_hx_a_scld
## 635 race_final_labelAsian pf_hx_a_scld
## 636 race_final_labelAsian/Pacific Islander pf_hx_a_scld
## 637 race_final_labelBlack pf_hx_a_scld
## 638 race_final_labelMore than one race pf_hx_a_scld
## 639 race_final_labelOther pf_hx_a_scld
## 640 race_final_labelUnknown/Not Reported pf_hx_a_scld
## 641 ethnicityNot Hispanic pf_hx_a_scld
## 642 ethnicityUnknown/Not Reported pf_hx_a_scld
## 643 ruralLiving in rural area pf_hx_a_scld
## 644 ruralUnknown/Not Reported pf_hx_a_scld
## 645 smokingSmoke or use vape pf_hx_a_scld
## 646 smokingUnknown/Not Reported pf_hx_a_scld
## 647 sq_drink_alcoholNo, former drinker (stopped) pf_hx_a_scld
## 648 sq_drink_alcoholUnknown/Not Reported pf_hx_a_scld
## 649 sq_drink_alcoholYes, current drinker pf_hx_a_scld
## 650 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_a_scld
## 651 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_a_scld
## 652 sq_average_drink_per_dayUnknown/Not Reported pf_hx_a_scld
## 653 sq_self_hep_bUnknown/Not Reported pf_hx_a_scld
## 654 sq_self_hep_bYes pf_hx_a_scld
## 655 sq_self_hep_cUnknown/Not Reported pf_hx_a_scld
## 656 sq_self_hep_cYes pf_hx_a_scld
## 657 supp_meds_tylenolUnknown/Not Reported pf_hx_a_scld
## 658 supp_meds_tylenolYes pf_hx_a_scld
## 659 supp_meds_steroidsUnknown/Not Reported pf_hx_a_scld
## 660 supp_meds_steroidsYes pf_hx_a_scld
## 661 sq_water_wellUnknown/Not Reported pf_hx_a_scld
## 662 sq_water_wellYes pf_hx_a_scld
## 663 sq_water_tap_unfilteredUnknown/Not Reported pf_hx_a_scld
## 664 sq_water_tap_unfilteredYes pf_hx_a_scld
## 665 sq_water_house_filtrationUnknown/Not Reported pf_hx_a_scld
## 666 sq_water_house_filtrationYes pf_hx_a_scld
## 667 sq_water_faucet_filterUnknown/Not Reported pf_hx_a_scld
## 668 sq_water_faucet_filterYes pf_hx_a_scld
## 669 sq_water_charcoal_filterUnknown/Not Reported pf_hx_a_scld
## 670 sq_water_charcoal_filterYes pf_hx_a_scld
## 671 sq_water_bottledUnknown/Not Reported pf_hx_a_scld
## 672 sq_water_bottledYes pf_hx_a_scld
## 673 sq_water_noneUnknown/Not Reported pf_hx_a_scld
## 674 sq_water_noneYes pf_hx_a_scld
## 675 sq_water_other_typeUnknown/Not Reported pf_hx_a_scld
## 676 sq_water_other_typeYes pf_hx_a_scld
## 677 sourceDUKE pfba_scld
## 678 sourceNCSU pfba_scld
## 679 sourceUNC pfba_scld
## 680 sexMale pfba_scld
## 681 race_eth_labelNHB pfba_scld
## 682 race_eth_labelNHO pfba_scld
## 683 race_eth_labelNHW pfba_scld
## 684 race_eth_labelUnknown/Not Reported pfba_scld
## 685 race_final_labelAmerican Indian pfba_scld
## 686 race_final_labelAmerican Indian/Alaskan Native pfba_scld
## 687 race_final_labelAsian pfba_scld
## 688 race_final_labelAsian/Pacific Islander pfba_scld
## 689 race_final_labelBlack pfba_scld
## 690 race_final_labelMore than one race pfba_scld
## 691 race_final_labelOther pfba_scld
## 692 race_final_labelUnknown/Not Reported pfba_scld
## 693 ethnicityNot Hispanic pfba_scld
## 694 ethnicityUnknown/Not Reported pfba_scld
## 695 ruralLiving in rural area pfba_scld
## 696 ruralUnknown/Not Reported pfba_scld
## 697 smokingSmoke or use vape pfba_scld
## 698 smokingUnknown/Not Reported pfba_scld
## 699 sq_drink_alcoholNo, former drinker (stopped) pfba_scld
## 700 sq_drink_alcoholUnknown/Not Reported pfba_scld
## 701 sq_drink_alcoholYes, current drinker pfba_scld
## 702 sq_average_drink_per_day1-2 alcoholic drinks per day pfba_scld
## 703 sq_average_drink_per_day3-4 alcoholic drinks per day pfba_scld
## 704 sq_average_drink_per_dayUnknown/Not Reported pfba_scld
## 705 sq_self_hep_bUnknown/Not Reported pfba_scld
## 706 sq_self_hep_bYes pfba_scld
## 707 sq_self_hep_cUnknown/Not Reported pfba_scld
## 708 sq_self_hep_cYes pfba_scld
## 709 supp_meds_tylenolUnknown/Not Reported pfba_scld
## 710 supp_meds_tylenolYes pfba_scld
## 711 supp_meds_steroidsUnknown/Not Reported pfba_scld
## 712 supp_meds_steroidsYes pfba_scld
## 713 sq_water_wellUnknown/Not Reported pfba_scld
## 714 sq_water_wellYes pfba_scld
## 715 sq_water_tap_unfilteredUnknown/Not Reported pfba_scld
## 716 sq_water_tap_unfilteredYes pfba_scld
## 717 sq_water_house_filtrationUnknown/Not Reported pfba_scld
## 718 sq_water_house_filtrationYes pfba_scld
## 719 sq_water_faucet_filterUnknown/Not Reported pfba_scld
## 720 sq_water_faucet_filterYes pfba_scld
## 721 sq_water_charcoal_filterUnknown/Not Reported pfba_scld
## 722 sq_water_charcoal_filterYes pfba_scld
## 723 sq_water_bottledUnknown/Not Reported pfba_scld
## 724 sq_water_bottledYes pfba_scld
## 725 sq_water_noneUnknown/Not Reported pfba_scld
## 726 sq_water_noneYes pfba_scld
## 727 sq_water_other_typeUnknown/Not Reported pfba_scld
## 728 sq_water_other_typeYes pfba_scld# Combine and print all results
all_results <- rbind(continuous_results, categorical_results)
print(all_results)## Confounders Coeff P
## 1 age_at_enrollment 0.02303963456 0.00002886969906048
## 2 bmi -0.01294812141 0.09611802745084295
## 3 trig_mg_d_l 0.00006026119 0.92894071463283046
## 4 age_at_enrollment 0.01401689114 0.01151280974826796
## 5 bmi -0.01953451965 0.01049324625441954
## 6 trig_mg_d_l -0.00087134772 0.19560135743360868
## 7 age_at_enrollment 0.02285242310 0.00003418692654294
## 8 bmi -0.01016268100 0.19162630103330469
## 9 trig_mg_d_l -0.00012960498 0.84543151201780353
## 10 age_at_enrollment 0.03015972790 0.00000003849922364
## 11 bmi -0.00632410823 0.39797483121906918
## 12 trig_mg_d_l -0.00060423845 0.36564464944825859
## 13 age_at_enrollment 0.00610856935 0.27012846280853531
## 14 bmi -0.00353210464 0.63621285405847949
## 15 trig_mg_d_l -0.00037474559 0.57810255513241993
## 16 age_at_enrollment -0.00039411970 0.94418956366096030
## 17 bmi 0.00294487490 0.67943161804413910
## 18 trig_mg_d_l -0.00051069442 0.45289086346786955
## 19 age_at_enrollment 0.01990379953 0.00032706545799936
## 20 bmi -0.01850220830 0.01730193122393180
## 21 trig_mg_d_l 0.00025406806 0.70465471480120978
## 22 age_at_enrollment 0.00422728729 0.41715088028540981
## 23 bmi -0.00405085209 0.60753830998090086
## 24 trig_mg_d_l -0.00053365608 0.39127771559889879
## 25 age_at_enrollment 0.01474075004 0.00829630670610244
## 26 bmi -0.02788390573 0.00016089041529066
## 27 trig_mg_d_l -0.00105386273 0.11863558257777872
## 28 age_at_enrollment 0.03647003290 0.00000000001254972
## 29 bmi -0.00498536122 0.49783998070122160
## 30 trig_mg_d_l 0.00035106604 0.59487932766741591
## 31 age_at_enrollment 0.00666942172 0.23478386028671305
## 32 bmi -0.02691555479 0.00053014713321087
## 33 trig_mg_d_l -0.00088518107 0.19110820055049224
## 34 age_at_enrollment 0.00723125430 0.18396989965273475
## 35 bmi -0.02551872568 0.00081610677927414
## 36 trig_mg_d_l -0.00011850725 0.86073672780481414
## 37 age_at_enrollment -0.00767217344 0.16771220778328327
## 38 bmi 0.00258183929 0.73804930867047069
## 39 trig_mg_d_l -0.00082944038 0.21370329383697809
## 40 age_at_enrollment 0.00156141004 0.75969057121585326
## 41 bmi -0.00261010632 0.73748118274195495
## 42 trig_mg_d_l -0.00015137036 0.80919075133267548
## 43 source 0.20089579824 0.17816637785405615
## 44 source 0.28773100400 0.02406241741592427
## 45 source -0.24039891626 0.34812012829126060
## 46 sex 0.18719769132 0.08219582251875361
## 47 race_eth_label 0.05078849200 0.84233629638900953
## 48 race_eth_label -0.11510427163 0.74149107535181924
## 49 race_eth_label 0.27422981845 0.26292213105598811
## 50 race_eth_label 0.03888580877 0.89888254812528012
## 51 race_final_label -0.12541197098 0.86037830965615614
## 52 race_final_label -0.56765432777 0.26261635127196636
## 53 race_final_label -0.09411026424 0.87184652297268461
## 54 race_final_label -0.40797332376 0.32604479871633008
## 55 race_final_label -0.20988620230 0.07137795642001048
## 56 race_final_label -0.26436616917 0.79275654669035089
## 57 race_final_label -0.29339427221 0.32387441802348305
## 58 race_final_label -0.14073047032 0.71483775593414878
## 59 ethnicity 0.18917716778 0.43536968718843172
## 60 ethnicity 0.03888580877 0.89927233218110325
## 61 rural -0.24655106670 0.18530426138296391
## 62 rural -0.14787709652 0.36453370981273614
## 63 smoking -0.32381027411 0.07305486987872799
## 64 smoking -0.22948580632 0.04908826819920185
## 65 sq_drink_alcohol -0.09194220808 0.55776878080576808
## 66 sq_drink_alcohol -0.22484955476 0.12238274703819219
## 67 sq_drink_alcohol -0.00556695961 0.96937817849957508
## 68 sq_average_drink_per_day 0.11608471601 0.65578352846413657
## 69 sq_average_drink_per_day -0.26182699346 0.53666952886876229
## 70 sq_average_drink_per_day -0.10529965991 0.40074255201137199
## 71 sq_self_hep_b -0.14193861153 0.21157942812386105
## 72 sq_self_hep_b 0.19215207005 0.44361557003895957
## 73 sq_self_hep_c -0.14660686900 0.19870154554602260
## 74 sq_self_hep_c -0.15523926134 0.52527191567200049
## 75 supp_meds_tylenol 0.30494095510 0.42547659640687141
## 76 supp_meds_tylenol 0.41908332375 0.50488516530129868
## 77 supp_meds_steroids 0.04092410029 0.90371151384786230
## 78 supp_meds_steroids -0.65705384429 0.53425913283768089
## 79 sq_water_well -0.20912105580 0.06225382264052396
## 80 sq_water_well -0.21812448525 0.15388307408751842
## 81 sq_water_tap_unfiltered -0.02362122330 0.86549139364514827
## 82 sq_water_tap_unfiltered 0.24293059625 0.06779584280841820
## 83 sq_water_house_filtration -0.16476832282 0.13133740209378553
## 84 sq_water_house_filtration -0.01594137263 0.93081104646967838
## 85 sq_water_faucet_filter -0.13799983326 0.25477840057299572
## 86 sq_water_faucet_filter 0.05896978212 0.65148799314023809
## 87 sq_water_charcoal_filter -0.15783530672 0.15697464465805316
## 88 sq_water_charcoal_filter 0.07812098769 0.61742210595572367
## 89 sq_water_bottled -0.48924546335 0.00049146516070939
## 90 sq_water_bottled -0.42565285199 0.00165296201584316
## 91 sq_water_none -0.15570725838 0.14497821238538158
## 92 sq_water_none -0.40194594145 0.10160522807343024
## 93 sq_water_other_type -0.14423859690 0.18408036183404880
## 94 sq_water_other_type -0.17196169235 0.40129154196832417
## 95 source -0.24632049084 0.09903735645483616
## 96 source -0.08968999822 0.48062363680237086
## 97 source -0.66412389592 0.00983867718366343
## 98 sex -0.13271702075 0.21831847824619438
## 99 race_eth_label 0.12471352645 0.62644193500230960
## 100 race_eth_label 0.42784565581 0.22209485749044836
## 101 race_eth_label 0.21907404512 0.37257706140278501
## 102 race_eth_label -0.05139885416 0.86705038235471044
## 103 race_final_label -0.12904195608 0.85548381268167228
## 104 race_final_label -0.48022761545 0.34013223492179612
## 105 race_final_label 0.22162573034 0.70227327313975429
## 106 race_final_label 0.73131111208 0.07689498379754887
## 107 race_final_label -0.10601570408 0.35862071309513155
## 108 race_final_label 1.83044970969 0.06776455127016796
## 109 race_final_label -0.18187519687 0.53812055995120500
## 110 race_final_label -0.42628443262 0.26576060685671926
## 111 ethnicity 0.19991584684 0.40915967427517430
## 112 ethnicity -0.05139885416 0.86694944980857691
## 113 rural -0.22321300609 0.23079719614026042
## 114 rural -0.08991929292 0.58161444509189819
## 115 smoking -0.47778649069 0.00796269280740328
## 116 smoking -0.26851843856 0.02067734371999552
## 117 sq_drink_alcohol -0.20787027884 0.17690069057809690
## 118 sq_drink_alcohol -0.13357448819 0.34875449247860013
## 119 sq_drink_alcohol 0.34307142692 0.01623256250436837
## 120 sq_average_drink_per_day -0.06581956258 0.79640617614150799
## 121 sq_average_drink_per_day 0.30971082539 0.45570924404054869
## 122 sq_average_drink_per_day -0.44982168589 0.00027963169345085
## 123 sq_self_hep_b -0.19517872802 0.08568444046719309
## 124 sq_self_hep_b 0.12479265729 0.61823627484439159
## 125 sq_self_hep_c -0.18857557102 0.09718837256862224
## 126 sq_self_hep_c 0.29382990208 0.22767940372032977
## 127 supp_meds_tylenol -0.10442649184 0.78494873278749755
## 128 supp_meds_tylenol 0.13718883533 0.82724883059649901
## 129 supp_meds_steroids -0.20962233873 0.53564564076426890
## 130 supp_meds_steroids -0.22238305274 0.83337724859734486
## 131 sq_water_well -0.13268719802 0.23787633145927936
## 132 sq_water_well -0.09790215802 0.52323933577829118
## 133 sq_water_tap_unfiltered -0.07182516640 0.60904913514095971
## 134 sq_water_tap_unfiltered 0.04709023866 0.72458169476826395
## 135 sq_water_house_filtration -0.11848550442 0.27832139702085046
## 136 sq_water_house_filtration -0.06928087412 0.70639209383938706
## 137 sq_water_faucet_filter -0.11121502339 0.35955630258942806
## 138 sq_water_faucet_filter -0.00361724267 0.97793718366327420
## 139 sq_water_charcoal_filter -0.11828454491 0.28976269305198821
## 140 sq_water_charcoal_filter -0.03045970838 0.84593988600083936
## 141 sq_water_bottled -0.33888418838 0.01623333367882546
## 142 sq_water_bottled -0.32623957681 0.01651137591491339
## 143 sq_water_none -0.08153190201 0.44683501274426518
## 144 sq_water_none -0.16116851633 0.51271635885510181
## 145 sq_water_other_type -0.14035669239 0.19632346742509224
## 146 sq_water_other_type 0.00574184469 0.97764091282821886
## 147 source -0.17536978665 0.24029175304654887
## 148 source -0.00155751589 0.99023454642433784
## 149 source -0.60420136676 0.01887733932902677
## 150 sex -0.18249153129 0.09022350808632779
## 151 race_eth_label 0.11143909821 0.66399374875135109
## 152 race_eth_label 0.07538354085 0.82971361421867285
## 153 race_eth_label 0.23515848590 0.33914499829760592
## 154 race_eth_label 0.01020295708 0.97352278690986982
## 155 race_final_label -0.03100524184 0.96535916817740675
## 156 race_final_label -0.43936740430 0.38636569597660375
## 157 race_final_label -0.01042341464 0.98576358357417404
## 158 race_final_label -0.11707470105 0.77822396547545969
## 159 race_final_label -0.11828239505 0.30947290327306004
## 160 race_final_label 1.41533588799 0.16063322156113585
## 161 race_final_label -0.23230174880 0.43522678347209232
## 162 race_final_label -0.25018833673 0.51661750631772252
## 163 ethnicity 0.19057081425 0.43185377958703053
## 164 ethnicity 0.01020295708 0.97349654239087935
## 165 rural -0.16657258549 0.37123860525779162
## 166 rural -0.12227732775 0.45397511461727769
## 167 smoking -0.55049716636 0.00222769302932113
## 168 smoking -0.24303361665 0.03579987987392344
## 169 sq_drink_alcohol -0.15214353078 0.32289373258876941
## 170 sq_drink_alcohol -0.06457873589 0.65051917779037560
## 171 sq_drink_alcohol 0.39510198704 0.00571563030051612
## 172 sq_average_drink_per_day -0.17936334767 0.48139369719176539
## 173 sq_average_drink_per_day 0.33975607179 0.41243345212405735
## 174 sq_average_drink_per_day -0.47566543041 0.00012072205421371
## 175 sq_self_hep_b -0.16998081239 0.13479409364303724
## 176 sq_self_hep_b 0.07765272744 0.75677422534221872
## 177 sq_self_hep_c -0.15324170559 0.17920855998162405
## 178 sq_self_hep_c -0.04152663629 0.86504317800143804
## 179 supp_meds_tylenol 0.13783174590 0.71761513785548414
## 180 supp_meds_tylenol 1.04553009228 0.09550981098712773
## 181 supp_meds_steroids -0.30899292899 0.36097388049451384
## 182 supp_meds_steroids 0.10145385531 0.92347692411988780
## 183 sq_water_well -0.19791095634 0.07794860503604970
## 184 sq_water_well -0.14505144638 0.34325528210841694
## 185 sq_water_tap_unfiltered -0.06685848275 0.63379349809383023
## 186 sq_water_tap_unfiltered 0.07774682125 0.56054814450977120
## 187 sq_water_house_filtration -0.15719663895 0.15011332667990274
## 188 sq_water_house_filtration -0.06359736630 0.72917960795943682
## 189 sq_water_faucet_filter -0.16765748230 0.16701503357658093
## 190 sq_water_faucet_filter -0.06287281614 0.63038370929637866
## 191 sq_water_charcoal_filter -0.17148290513 0.12453939373143023
## 192 sq_water_charcoal_filter -0.08827373415 0.57281724091754371
## 193 sq_water_bottled -0.31695071374 0.02481255320482565
## 194 sq_water_bottled -0.25741602293 0.05871655083207015
## 195 sq_water_none -0.15039524262 0.15986807660112737
## 196 sq_water_none -0.28059297095 0.25347435646347488
## 197 sq_water_other_type -0.17879316843 0.09958577804326338
## 198 sq_water_other_type -0.06326299693 0.75719660380250520
## 199 source -0.02812398302 0.85071911056610372
## 200 source 0.06545950319 0.60757504689611586
## 201 source -0.54934387889 0.03293039529831022
## 202 sex 0.17018018712 0.11421477651813232
## 203 race_eth_label 0.17735142739 0.48947961492581005
## 204 race_eth_label 0.21368881066 0.54222117666899239
## 205 race_eth_label 0.22072926125 0.36962368798292344
## 206 race_eth_label -0.06425467746 0.83445298522020916
## 207 race_final_label -0.25650160083 0.71719909410729121
## 208 race_final_label -0.61212402402 0.22393436309559747
## 209 race_final_label 0.30221826119 0.60200810793987869
## 210 race_final_label 0.34506062946 0.40285038365018688
## 211 race_final_label -0.03033696946 0.79256603065346942
## 212 race_final_label 2.50633119704 0.01250591825764212
## 213 race_final_label -0.26150115907 0.37590551468124966
## 214 race_final_label -0.35693228717 0.35102751784317132
## 215 ethnicity 0.20729582419 0.39191644876826492
## 216 ethnicity -0.06425467746 0.83404410390462613
## 217 rural -0.15050379014 0.41945767850033333
## 218 rural -0.02567749874 0.87510243365735252
## 219 smoking -0.56816147223 0.00162483462451516
## 220 smoking -0.17764283744 0.12484388703392600
## 221 sq_drink_alcohol -0.09279790313 0.55221901412799523
## 222 sq_drink_alcohol -0.03617257237 0.80250707464714910
## 223 sq_drink_alcohol 0.25098982175 0.08264764890083667
## 224 sq_average_drink_per_day -0.14733130408 0.56878498930871513
## 225 sq_average_drink_per_day 0.13741276657 0.74388410618383627
## 226 sq_average_drink_per_day -0.30905433609 0.01330787645466807
## 227 sq_self_hep_b -0.09608172406 0.39765419685309278
## 228 sq_self_hep_b 0.27321564105 0.27638203097614938
## 229 sq_self_hep_c -0.08827848999 0.43852931773111470
## 230 sq_self_hep_c 0.26631945107 0.27592232776750170
## 231 supp_meds_tylenol 0.22225409868 0.56129273508638611
## 232 supp_meds_tylenol 0.49018745604 0.43550301353660792
## 233 supp_meds_steroids -0.00137046605 0.99676971448586982
## 234 supp_meds_steroids -0.08300121298 0.93744563737665432
## 235 sq_water_well -0.13821435834 0.21869101547986863
## 236 sq_water_well -0.14605474755 0.34083898263584533
## 237 sq_water_tap_unfiltered -0.10884128949 0.43862900238366409
## 238 sq_water_tap_unfiltered -0.03152028087 0.81362118915627635
## 239 sq_water_house_filtration -0.10144195077 0.35332742478950041
## 240 sq_water_house_filtration 0.02881244426 0.87552600125445079
## 241 sq_water_faucet_filter -0.14451134890 0.23371706850434837
## 242 sq_water_faucet_filter -0.11576443844 0.37605746039474852
## 243 sq_water_charcoal_filter -0.09435771005 0.39811618261251014
## 244 sq_water_charcoal_filter 0.07837485262 0.61705027014982561
## 245 sq_water_bottled -0.37438959384 0.00776080774017603
## 246 sq_water_bottled -0.39800413798 0.00339692534565435
## 247 sq_water_none -0.09715229470 0.36444330562043414
## 248 sq_water_none -0.22134405610 0.36842362123646244
## 249 sq_water_other_type -0.11865182176 0.27482381292786012
## 250 sq_water_other_type -0.12858001415 0.53059508137585887
## 251 source 0.10434243584 0.48142224323953187
## 252 source -0.25317070261 0.04569477728082912
## 253 source 0.36671855653 0.15030326977519787
## 254 sex 0.04753972206 0.65952777755572933
## 255 race_eth_label -0.29217269122 0.24558725047749871
## 256 race_eth_label 0.25904064681 0.45098650537993790
## 257 race_eth_label 0.19739330324 0.41292192888074941
## 258 race_eth_label -0.00442983508 0.98826938151728350
## 259 race_final_label 2.99035334157 0.00001177078990472
## 260 race_final_label -0.75076319456 0.11711937574190467
## 261 race_final_label -0.21362555587 0.69833970735643969
## 262 race_final_label -0.20106093275 0.60825038534995124
## 263 race_final_label -0.52810627333 0.00000214067537662
## 264 race_final_label 0.14446620325 0.87920452393557480
## 265 race_final_label -0.54187633507 0.05422224390061022
## 266 race_final_label -0.64172021528 0.07841348875071134
## 267 ethnicity 0.05211948502 0.82999524103981692
## 268 ethnicity -0.00442983508 0.98850968884498225
## 269 rural 0.03981842586 0.83089199323123042
## 270 rural 0.06732367912 0.68047191960674125
## 271 smoking -0.25860857221 0.15406242068696158
## 272 smoking -0.04266261101 0.71521597801003944
## 273 sq_drink_alcohol 0.13586280492 0.38818440115655084
## 274 sq_drink_alcohol 0.04809961613 0.74153340009344393
## 275 sq_drink_alcohol 0.06560293585 0.65217578709575408
## 276 sq_average_drink_per_day 0.34358261099 0.18358567488178576
## 277 sq_average_drink_per_day 1.13305976415 0.00724023699402287
## 278 sq_average_drink_per_day 0.11291955180 0.36326518486072301
## 279 sq_self_hep_b -0.00524517482 0.96324302106208015
## 280 sq_self_hep_b -0.15705182864 0.53229219046349652
## 281 sq_self_hep_c 0.07666664632 0.50144498984545860
## 282 sq_self_hep_c 0.25616463159 0.29506967023341613
## 283 supp_meds_tylenol 0.14236624332 0.70995004114878091
## 284 supp_meds_tylenol 0.09558088307 0.87917492730682845
## 285 supp_meds_steroids 0.10616093523 0.75379520356562613
## 286 supp_meds_steroids -0.00628231156 0.99525978948954863
## 287 sq_water_well -0.07901703786 0.48242764822962969
## 288 sq_water_well -0.02984035320 0.84588876056705242
## 289 sq_water_tap_unfiltered 0.03916307547 0.78055819320376185
## 290 sq_water_tap_unfiltered 0.05959691935 0.65602092727267491
## 291 sq_water_house_filtration -0.06483291389 0.55318487403520389
## 292 sq_water_house_filtration 0.04468035213 0.80820617839498143
## 293 sq_water_faucet_filter 0.04534724923 0.70845488436320547
## 294 sq_water_faucet_filter 0.15306167211 0.24212063939652043
## 295 sq_water_charcoal_filter -0.08703304619 0.43594263519054810
## 296 sq_water_charcoal_filter -0.13294540628 0.39671140672906335
## 297 sq_water_bottled -0.16259904098 0.25016970657882531
## 298 sq_water_bottled -0.21830266548 0.10999177430940106
## 299 sq_water_none -0.05212974655 0.62687470346804519
## 300 sq_water_none 0.04128376170 0.86689577335487455
## 301 sq_water_other_type -0.08433147055 0.43780014503922282
## 302 sq_water_other_type 0.05985138573 0.77047252115801956
## 303 source -0.24209283532 0.10550138874091017
## 304 source -0.31476257326 0.01381394218896274
## 305 source -0.43870902249 0.08785424567301257
## 306 sex -0.07419888249 0.49160923227274667
## 307 race_eth_label -0.01464216199 0.95432754792565500
## 308 race_eth_label -0.02338660368 0.94662591167007082
## 309 race_eth_label 0.21620594043 0.37788131612548015
## 310 race_eth_label 0.29446925111 0.33681006421286097
## 311 race_final_label -0.06497780823 0.92729153707792866
## 312 race_final_label -0.36498230103 0.47055950006093461
## 313 race_final_label -0.28720334611 0.62210940950523153
## 314 race_final_label -0.23482160095 0.57120995522040818
## 315 race_final_label -0.26151753168 0.02465261727798186
## 316 race_final_label -0.32212450954 0.74855511160559651
## 317 race_final_label -0.27609206683 0.35256225658706697
## 318 race_final_label -0.33436959282 0.38492197181345200
## 319 ethnicity 0.13563843592 0.57595190803645446
## 320 ethnicity 0.29446925111 0.33814572280041677
## 321 rural 0.40223577490 0.03046477151052450
## 322 rural -0.02546298067 0.87544973896637868
## 323 smoking 0.02876208111 0.87397920554472974
## 324 smoking -0.13222001617 0.25890118865789558
## 325 sq_drink_alcohol 0.03093608928 0.84399176612941307
## 326 sq_drink_alcohol -0.11354395865 0.43588771007606608
## 327 sq_drink_alcohol 0.04248949045 0.77007949378540197
## 328 sq_average_drink_per_day 0.00173849948 0.99459668702331505
## 329 sq_average_drink_per_day 1.43403530887 0.00065732225553094
## 330 sq_average_drink_per_day 0.00140892991 0.99089640548374591
## 331 sq_self_hep_b -0.16164730427 0.15490547435844459
## 332 sq_self_hep_b -0.23109631067 0.35688641562230339
## 333 sq_self_hep_c -0.11062049213 0.33226578512771388
## 334 sq_self_hep_c 0.11736859792 0.63123268162752577
## 335 supp_meds_tylenol 0.05111422485 0.89376908860933635
## 336 supp_meds_tylenol -0.10000151347 0.87363945925546149
## 337 supp_meds_steroids 0.06764799759 0.84159227516948099
## 338 supp_meds_steroids -0.13545178545 0.89808056927624569
## 339 sq_water_well 0.02866859932 0.79878534040690352
## 340 sq_water_well -0.08368273867 0.58576338919294979
## 341 sq_water_tap_unfiltered -0.04878699378 0.72768354490860843
## 342 sq_water_tap_unfiltered -0.19626578122 0.14160342074544699
## 343 sq_water_house_filtration 0.06616698617 0.54472537125352871
## 344 sq_water_house_filtration 0.18374184953 0.31807308447128430
## 345 sq_water_faucet_filter 0.08488801129 0.48456664213754364
## 346 sq_water_faucet_filter 0.05917333707 0.65126163583327279
## 347 sq_water_charcoal_filter 0.06196009253 0.57934760076694691
## 348 sq_water_charcoal_filter 0.08690500377 0.57974234090501586
## 349 sq_water_bottled 0.00150677405 0.99150577538440487
## 350 sq_water_bottled -0.11319237198 0.40754821004133213
## 351 sq_water_none 0.03361244212 0.75393115719870929
## 352 sq_water_none -0.08365917336 0.73415922696710800
## 353 sq_water_other_type 0.02430377718 0.82315529229054696
## 354 sq_water_other_type -0.04733327578 0.81768277140597645
## 355 source -0.07628426924 0.61053968426051308
## 356 source 0.06932016021 0.58737751970846286
## 357 source -0.42856769023 0.09643431102686301
## 358 sex -0.12516480332 0.24571033496867928
## 359 race_eth_label -0.14944647943 0.55373366719946415
## 360 race_eth_label 0.13817704270 0.68861553399694753
## 361 race_eth_label 0.29883181637 0.21707705491636420
## 362 race_eth_label 0.01456195611 0.96159120427070899
## 363 race_final_label 0.23361134664 0.73943226114684735
## 364 race_final_label -0.68257158805 0.17165331000666206
## 365 race_final_label 0.06701195493 0.90716829723055903
## 366 race_final_label 0.01299508731 0.97464817278574101
## 367 race_final_label -0.43096670466 0.00019045927418025
## 368 race_final_label 0.54236038999 0.58429976766167235
## 369 race_final_label -0.30030274488 0.30534074418752660
## 370 race_final_label 0.02574794177 0.94588993724285442
## 371 ethnicity 0.15605957242 0.51999177754676029
## 372 ethnicity 0.01456195611 0.96220473628700909
## 373 rural -0.19014848628 0.30702073717271200
## 374 rural -0.17063001679 0.29578828788986516
## 375 smoking -0.40978883374 0.02321464375482415
## 376 smoking -0.22263200726 0.05574471466387071
## 377 sq_drink_alcohol -0.11149608945 0.46913030842951009
## 378 sq_drink_alcohol -0.04673299431 0.74326861358173835
## 379 sq_drink_alcohol 0.40955746795 0.00422640049175355
## 380 sq_average_drink_per_day -0.04042465331 0.87408657104043130
## 381 sq_average_drink_per_day -0.26009324830 0.53096833080426087
## 382 sq_average_drink_per_day -0.48147462903 0.00010257804469636
## 383 sq_self_hep_b -0.17885175294 0.11559742974387974
## 384 sq_self_hep_b 0.04522819244 0.85681327359605108
## 385 sq_self_hep_c -0.17786660733 0.11880220685086884
## 386 sq_self_hep_c 0.00515648437 0.98314670068817211
## 387 supp_meds_tylenol 0.44380109830 0.24563690847941486
## 388 supp_meds_tylenol 0.77050797170 0.21985255079337265
## 389 supp_meds_steroids 0.10780508159 0.75003621633080930
## 390 supp_meds_steroids -0.38080944008 0.71869901424882265
## 391 sq_water_well -0.18653694538 0.09675928899760877
## 392 sq_water_well -0.11223939809 0.46350632851723095
## 393 sq_water_tap_unfiltered -0.00717499280 0.95913876245625251
## 394 sq_water_tap_unfiltered 0.17549299403 0.18845245498274266
## 395 sq_water_house_filtration -0.16064803523 0.14111864812130245
## 396 sq_water_house_filtration -0.18414678866 0.31607063806374974
## 397 sq_water_faucet_filter -0.18099109271 0.13559656249832411
## 398 sq_water_faucet_filter -0.14435720063 0.26925087582704033
## 399 sq_water_charcoal_filter -0.17126417939 0.12499948414919104
## 400 sq_water_charcoal_filter -0.04422335533 0.77750970889267790
## 401 sq_water_bottled -0.43728254052 0.00182086074126584
## 402 sq_water_bottled -0.45962196552 0.00069540024129137
## 403 sq_water_none -0.16543176316 0.12150146864343667
## 404 sq_water_none -0.39284391304 0.10950059241961881
## 405 sq_water_other_type -0.18103712113 0.09537905447063832
## 406 sq_water_other_type -0.03766610528 0.85393082686462518
## 407 source -0.59790103757 0.00005228868616012
## 408 source -0.26557025946 0.03370576474755375
## 409 source 0.32661198117 0.19403427471930992
## 410 sex -0.15368685249 0.15386599131093590
## 411 race_eth_label 0.07231545684 0.77714097293777329
## 412 race_eth_label 0.47425865769 0.17483025510465880
## 413 race_eth_label 0.08698417548 0.72246058781372169
## 414 race_eth_label 0.45767860326 0.13554981673753960
## 415 race_final_label 0.34729696079 0.62303943070382883
## 416 race_final_label 1.65297624760 0.00107063992871887
## 417 race_final_label -0.07238566712 0.90034178273734011
## 418 race_final_label -0.04129806505 0.92002234501068081
## 419 race_final_label 0.06298670916 0.58422548881022029
## 420 race_final_label -0.25891127952 0.79509259208137861
## 421 race_final_label -0.20709863531 0.48201712804660735
## 422 race_final_label -0.22782367745 0.55058320024782048
## 423 ethnicity 0.09994099180 0.67927159271312165
## 424 ethnicity 0.45767860326 0.13566211156981897
## 425 rural -0.14723144214 0.42822635251511909
## 426 rural -0.26072761706 0.11000532282995749
## 427 smoking 0.21611201277 0.23338823688726526
## 428 smoking -0.06618776224 0.57137978178831528
## 429 sq_drink_alcohol 0.10156874252 0.51822776011725313
## 430 sq_drink_alcohol -0.00190253764 0.98957692131135044
## 431 sq_drink_alcohol 0.15143861403 0.29776677122813072
## 432 sq_average_drink_per_day 0.05748739112 0.82519940509489453
## 433 sq_average_drink_per_day -0.34492434674 0.41554963544339829
## 434 sq_average_drink_per_day -0.13243173333 0.29051139953655003
## 435 sq_self_hep_b -0.12772680791 0.26138986317014279
## 436 sq_self_hep_b -0.18926614933 0.45099558920554317
## 437 sq_self_hep_c -0.12436403200 0.27562127705553680
## 438 sq_self_hep_c -0.19167028106 0.43308498300754394
## 439 supp_meds_tylenol 0.25950328321 0.49765332661943340
## 440 supp_meds_tylenol 0.07771900517 0.90156876480204828
## 441 supp_meds_steroids 0.21785483823 0.51959775927009866
## 442 supp_meds_steroids -0.29015095263 0.78364104048900063
## 443 sq_water_well -0.03539646609 0.75308976935880034
## 444 sq_water_well -0.00403742287 0.97903005717147185
## 445 sq_water_tap_unfiltered -0.22763959852 0.10460564698569111
## 446 sq_water_tap_unfiltered -0.20965868166 0.11625097875513281
## 447 sq_water_house_filtration 0.03180003008 0.76900375265055310
## 448 sq_water_house_filtration 0.49963711198 0.00639538693209372
## 449 sq_water_faucet_filter -0.00731918832 0.95193128359767076
## 450 sq_water_faucet_filter 0.07300597137 0.57712410414268367
## 451 sq_water_charcoal_filter -0.02645028662 0.81297502605540750
## 452 sq_water_charcoal_filter 0.03855810651 0.80596269263470566
## 453 sq_water_bottled -0.12807664111 0.36598943670439332
## 454 sq_water_bottled -0.10002749761 0.46439951786494849
## 455 sq_water_none -0.00134633678 0.98995994083384609
## 456 sq_water_none -0.33307110634 0.17591982687334426
## 457 sq_water_other_type 0.02991785344 0.78311655900996846
## 458 sq_water_other_type 0.14932828872 0.46695697882131204
## 459 source -0.12233567491 0.40741994614468358
## 460 source 0.16665102186 0.18606871857653695
## 461 source -0.71085759563 0.00529967763418086
## 462 sex -0.08107283790 0.45234435016075591
## 463 race_eth_label 0.38843641214 0.12719466758624104
## 464 race_eth_label 0.75496304658 0.03031047612559701
## 465 race_eth_label 0.30938744827 0.20479634375196532
## 466 race_eth_label -0.06964204857 0.81923914578153800
## 467 race_final_label -0.54217003211 0.43999328372661040
## 468 race_final_label -0.49473597649 0.32117620165951011
## 469 race_final_label 0.31984176703 0.57762843834789201
## 470 race_final_label 1.39032924586 0.00073512345398631
## 471 race_final_label 0.08140436173 0.47660773266544987
## 472 race_final_label 0.53300229345 0.59053852024600917
## 473 race_final_label -0.22594287753 0.44010936421997648
## 474 race_final_label -0.56024015523 0.14005245576397632
## 475 ethnicity 0.35330235643 0.14289994452075006
## 476 ethnicity -0.06964204857 0.81947623415181903
## 477 rural -0.21079073860 0.25793964453798679
## 478 rural -0.04719442936 0.77247965322541445
## 479 smoking -0.55938000641 0.00181874007453686
## 480 smoking -0.32017764170 0.00560773476027302
## 481 sq_drink_alcohol -0.17786232689 0.24777442714207512
## 482 sq_drink_alcohol -0.16663937622 0.24258750496615397
## 483 sq_drink_alcohol 0.34004163394 0.01719459843915620
## 484 sq_average_drink_per_day 0.16709286937 0.51266621906039722
## 485 sq_average_drink_per_day 0.03513635302 0.93254748917459640
## 486 sq_average_drink_per_day -0.42766182037 0.00054676111079222
## 487 sq_self_hep_b -0.22785512960 0.04436660508739628
## 488 sq_self_hep_b 0.24387552625 0.32895313970867956
## 489 sq_self_hep_c -0.23110609461 0.04207291408647388
## 490 sq_self_hep_c 0.22064001249 0.36434293822168251
## 491 supp_meds_tylenol -0.32443230158 0.39565811530909123
## 492 supp_meds_tylenol 0.26527944634 0.67233383417726300
## 493 supp_meds_steroids -0.43690149238 0.19622409137501412
## 494 supp_meds_steroids -0.02186261809 0.98346705424688374
## 495 sq_water_well -0.12817120720 0.25427262322351152
## 496 sq_water_well -0.02495098293 0.87074988658916408
## 497 sq_water_tap_unfiltered -0.22205510583 0.11348990546404761
## 498 sq_water_tap_unfiltered -0.07001753613 0.59948647097832886
## 499 sq_water_house_filtration -0.13263834075 0.22481218952720550
## 500 sq_water_house_filtration -0.03978205473 0.82866854646978583
## 501 sq_water_faucet_filter -0.13144880015 0.27842603953196615
## 502 sq_water_faucet_filter 0.02976012029 0.81982321014451420
## 503 sq_water_charcoal_filter -0.11479817257 0.30392858519815302
## 504 sq_water_charcoal_filter 0.05286413942 0.73582611255727159
## 505 sq_water_bottled -0.35114193595 0.01283108050418380
## 506 sq_water_bottled -0.28139780657 0.03857335714816601
## 507 sq_water_none -0.09645572879 0.36794161779746204
## 508 sq_water_none -0.20912526088 0.39547290349843378
## 509 sq_water_other_type -0.13920849554 0.20007125981871401
## 510 sq_water_other_type -0.07811881114 0.70304722582391288
## 511 source 0.06168004872 0.67844612713381902
## 512 source 0.22832573243 0.07252736028874485
## 513 source -0.45648133987 0.07474208092999160
## 514 sex 0.30350051034 0.00469493718683770
## 515 race_eth_label 0.17144822696 0.50197497862570550
## 516 race_eth_label -0.08286614721 0.81223970154000491
## 517 race_eth_label 0.35914307931 0.14277455985685836
## 518 race_eth_label 0.17094081549 0.57643705503039200
## 519 race_final_label -0.35595703100 0.61712705883957342
## 520 race_final_label -0.75988071120 0.13339352193114282
## 521 race_final_label -0.06325872262 0.91352122741302155
## 522 race_final_label -0.37547025156 0.36528543014309145
## 523 race_final_label -0.16785923828 0.14835335109303602
## 524 race_final_label 1.00801708607 0.31601277293424263
## 525 race_final_label -0.25448828409 0.39135877967638943
## 526 race_final_label -0.27232894289 0.47898862867843928
## 527 ethnicity 0.28253434133 0.24394937568137418
## 528 ethnicity 0.17094081549 0.57772753504935903
## 529 rural -0.15330859096 0.41080517756177970
## 530 rural -0.05287271038 0.74619014229260427
## 531 smoking -0.59743152776 0.00092276549007707
## 532 smoking -0.12179225914 0.29205421769068646
## 533 sq_drink_alcohol -0.06535445769 0.67660542380994215
## 534 sq_drink_alcohol 0.01175053534 0.93549031572167918
## 535 sq_drink_alcohol 0.20806866087 0.15152091369328727
## 536 sq_average_drink_per_day 0.01595902681 0.95098333148843639
## 537 sq_average_drink_per_day 0.06376641718 0.88000354530749192
## 538 sq_average_drink_per_day -0.21608972737 0.08417452030592833
## 539 sq_self_hep_b -0.02247278890 0.84347580470059136
## 540 sq_self_hep_b 0.14660302112 0.55989141502025297
## 541 sq_self_hep_c -0.02853443225 0.80272136125276106
## 542 sq_self_hep_c 0.02671288685 0.91313950657492171
## 543 supp_meds_tylenol 0.30269349191 0.42836384684072104
## 544 supp_meds_tylenol 0.78143889628 0.21353816457676289
## 545 supp_meds_steroids -0.10559143425 0.75503614022516952
## 546 supp_meds_steroids -0.46625437338 0.65925938965952646
## 547 sq_water_well -0.06777253946 0.54651262733604811
## 548 sq_water_well -0.15540013737 0.31136152736212841
## 549 sq_water_tap_unfiltered -0.00727570282 0.95872659269934801
## 550 sq_water_tap_unfiltered 0.02603227415 0.84574417416999270
## 551 sq_water_house_filtration -0.03605705995 0.74164992928474538
## 552 sq_water_house_filtration -0.01642026493 0.92895252444544685
## 553 sq_water_faucet_filter -0.07838889488 0.51835547757238953
## 554 sq_water_faucet_filter -0.12496215620 0.33973975540600310
## 555 sq_water_charcoal_filter -0.04281037697 0.70176869616976578
## 556 sq_water_charcoal_filter 0.02425610311 0.87718241871528879
## 557 sq_water_bottled -0.34343405338 0.01441692733043675
## 558 sq_water_bottled -0.43313180106 0.00143238137611300
## 559 sq_water_none -0.03958620288 0.71147423698221934
## 560 sq_water_none -0.32045363493 0.19292650115117382
## 561 sq_water_other_type -0.04449232868 0.68223041086397185
## 562 sq_water_other_type -0.16656898382 0.41705478475211033
## 563 source 0.00341597575 0.98179146397574657
## 564 source 0.04502069166 0.72434393181777690
## 565 source -0.51200370343 0.04708117779278184
## 566 sex -0.02359390772 0.82693773382376023
## 567 race_eth_label 0.06086368697 0.81187103175202535
## 568 race_eth_label 0.28356857907 0.41725220517750161
## 569 race_eth_label 0.17080174030 0.48604179615837562
## 570 race_eth_label -0.23994111834 0.43384263785675281
## 571 race_final_label -0.46259942334 0.51535911876491247
## 572 race_final_label -0.48699606048 0.33501616397590472
## 573 race_final_label 0.00662795737 0.99090900750855360
## 574 race_final_label 0.76233095269 0.06613992103282181
## 575 race_final_label -0.10757306829 0.35324429414612923
## 576 race_final_label -0.15142758139 0.88002788778056229
## 577 race_final_label -0.07631609842 0.79678214569844263
## 578 race_final_label -0.54040796213 0.15992207734304267
## 579 ethnicity 0.14262137861 0.55500838863838109
## 580 ethnicity -0.23994111834 0.43331808561261076
## 581 rural -0.13286357000 0.47604978557855959
## 582 rural 0.00406386978 0.98015694799679176
## 583 smoking -0.33925333501 0.06089981157603477
## 584 smoking -0.16043150025 0.16912075431887635
## 585 sq_drink_alcohol 0.03369684146 0.82844187593956276
## 586 sq_drink_alcohol 0.03928903694 0.78511449567257685
## 587 sq_drink_alcohol 0.37799051426 0.00887878150753327
## 588 sq_average_drink_per_day -0.14649866148 0.56947537328377917
## 589 sq_average_drink_per_day -0.00615415023 0.98827941703331035
## 590 sq_average_drink_per_day -0.37744411216 0.00245565678435985
## 591 sq_self_hep_b -0.14105158765 0.21493465638586645
## 592 sq_self_hep_b 0.04210692449 0.86677177612829825
## 593 sq_self_hep_c -0.09979353088 0.37976264387495307
## 594 sq_self_hep_c 0.42840676793 0.07911871955041586
## 595 supp_meds_tylenol -0.24174331541 0.52552310304422201
## 596 supp_meds_tylenol 0.74300215321 0.23522327501678239
## 597 supp_meds_steroids -0.21852365905 0.51797858027497301
## 598 supp_meds_steroids 0.71454274816 0.49868186690490035
## 599 sq_water_well 0.01168605994 0.91721458430984049
## 600 sq_water_well 0.12608602047 0.41156166219780010
## 601 sq_water_tap_unfiltered -0.09220049374 0.51184853848187739
## 602 sq_water_tap_unfiltered -0.07543539858 0.57279182355582148
## 603 sq_water_house_filtration -0.01192867507 0.91310090818639877
## 604 sq_water_house_filtration -0.12977075568 0.48093371950682218
## 605 sq_water_faucet_filter -0.02728505772 0.82219076468470564
## 606 sq_water_faucet_filter -0.09022857915 0.49076539977288947
## 607 sq_water_charcoal_filter 0.03682718974 0.74186846476577573
## 608 sq_water_charcoal_filter 0.04040313633 0.79688719547918652
## 609 sq_water_bottled -0.17773609857 0.20792533592251639
## 610 sq_water_bottled -0.27538320757 0.04357849187013037
## 611 sq_water_none 0.02229770786 0.83524455521134733
## 612 sq_water_none -0.12918736057 0.59997849432490002
## 613 sq_water_other_type -0.06032328779 0.57889504928244273
## 614 sq_water_other_type 0.09236555500 0.65262891764171804
## 615 source 0.12846146544 0.38687468700134564
## 616 source 0.39720909646 0.00181891699048696
## 617 source 0.04155554741 0.87053458291733876
## 618 sex 0.08119651991 0.45165441374253601
## 619 race_eth_label -0.00586181941 0.98166500714231375
## 620 race_eth_label 0.07238360316 0.83548591495916558
## 621 race_eth_label 0.27902406267 0.25418843077719411
## 622 race_eth_label 0.04716044722 0.87739425723636211
## 623 race_final_label 1.36028732641 0.05400990074290221
## 624 race_final_label -0.67792247840 0.17574960546629939
## 625 race_final_label -0.05066729394 0.92993187406726230
## 626 race_final_label -0.43348449368 0.29078243295853851
## 627 race_final_label -0.30480826458 0.00818424840147474
## 628 race_final_label 0.21183589124 0.83120895797991856
## 629 race_final_label -0.36639427441 0.21245914434554627
## 630 race_final_label -0.37159837289 0.32895717410028713
## 631 ethnicity 0.18359567650 0.44910052978292048
## 632 ethnicity 0.04716044722 0.87801175679233379
## 633 rural -0.29369835283 0.11397432465805926
## 634 rural -0.22595188987 0.16528201759409997
## 635 smoking -0.32812133861 0.07025007004597797
## 636 smoking -0.08647518574 0.45886310938747255
## 637 sq_drink_alcohol 0.00872138962 0.95557605731020900
## 638 sq_drink_alcohol 0.05329752766 0.71336276014425226
## 639 sq_drink_alcohol 0.26418668784 0.06864932704252635
## 640 sq_average_drink_per_day 0.34598625878 0.18167914444426966
## 641 sq_average_drink_per_day -0.07706509184 0.85474769829995156
## 642 sq_average_drink_per_day -0.18716258000 0.13320907616193359
## 643 sq_self_hep_b -0.04384126872 0.70010452722554173
## 644 sq_self_hep_b -0.14852356441 0.55475236942286110
## 645 sq_self_hep_c -0.00850483650 0.94062361844988573
## 646 sq_self_hep_c -0.13325302642 0.58632012495494368
## 647 supp_meds_tylenol 0.18150755550 0.63535705748587723
## 648 supp_meds_tylenol 0.16741359119 0.79002340503005719
## 649 supp_meds_steroids 0.16256745203 0.63085743718607123
## 650 supp_meds_steroids -0.40299902397 0.70298916766313346
## 651 sq_water_well -0.03177598964 0.77734871293318042
## 652 sq_water_well -0.16152694531 0.29269446086609541
## 653 sq_water_tap_unfiltered 0.13088726281 0.34954163790135262
## 654 sq_water_tap_unfiltered 0.26544081020 0.04667168825859383
## 655 sq_water_house_filtration 0.03539716070 0.74609488302110494
## 656 sq_water_house_filtration 0.12286695845 0.50458013295510473
## 657 sq_water_faucet_filter 0.07422498204 0.54112145438894954
## 658 sq_water_faucet_filter 0.04600188469 0.72532178061484442
## 659 sq_water_charcoal_filter -0.01701590491 0.87904866822130390
## 660 sq_water_charcoal_filter -0.00714787774 0.96368572588700163
## 661 sq_water_bottled -0.30337952011 0.02943808106098152
## 662 sq_water_bottled -0.52713535708 0.00009910794049467
## 663 sq_water_none 0.03220906487 0.76347440250852572
## 664 sq_water_none -0.28637917545 0.24456352714546090
## 665 sq_water_other_type -0.00069820403 0.99487761581831213
## 666 sq_water_other_type 0.04767553387 0.81640595739469646
## 667 source 0.51882522422 0.00035296753016782
## 668 source 0.27329328029 0.02652537691163277
## 669 source 1.36562416595 0.00000006218287011
## 670 sex -0.15256425529 0.15690319949754505
## 671 race_eth_label -0.09961913738 0.69832721067772541
## 672 race_eth_label -0.28068732935 0.42434022335421961
## 673 race_eth_label -0.15502571296 0.52926880834615642
## 674 race_eth_label -0.24017983164 0.43570815119399686
## 675 race_final_label 0.36314576107 0.60936886944372626
## 676 race_final_label -0.12549586113 0.80359047025394681
## 677 race_final_label 0.08595421817 0.88247237807532541
## 678 race_final_label -0.55771131397 0.17818156233011151
## 679 race_final_label 0.05714150773 0.62161421926663007
## 680 race_final_label 2.15432621489 0.03221506802430414
## 681 race_final_label -0.13260894822 0.65440808798253147
## 682 race_final_label 0.32256034411 0.40092858744280124
## 683 ethnicity -0.14391451569 0.55309406174542874
## 684 ethnicity -0.24017983164 0.43478718661802029
## 685 rural -0.03072806294 0.86877802201807885
## 686 rural 0.22724981485 0.16396371383201136
## 687 smoking 0.38382346020 0.03156290235866580
## 688 smoking 0.42443224230 0.00024963802756887
## 689 sq_drink_alcohol -0.10651007576 0.49296709044419185
## 690 sq_drink_alcohol 0.29694661582 0.03963369285390450
## 691 sq_drink_alcohol -0.08549871620 0.55172036530077662
## 692 sq_average_drink_per_day -0.10442762134 0.68793945814009794
## 693 sq_average_drink_per_day -0.27319094465 0.51845922867124128
## 694 sq_average_drink_per_day 0.13982610455 0.26388573205592636
## 695 sq_self_hep_b 0.33867951731 0.00270288931958967
## 696 sq_self_hep_b -0.23822011787 0.33688740607011625
## 697 sq_self_hep_c 0.38630395960 0.00064482875448686
## 698 sq_self_hep_c 0.33973550067 0.15897129077815536
## 699 supp_meds_tylenol -0.03979105231 0.91717244608161719
## 700 supp_meds_tylenol -0.35438704003 0.57295049357703465
## 701 supp_meds_steroids 0.17348130782 0.60813457682162686
## 702 supp_meds_steroids 0.65707774780 0.53424295732899529
## 703 sq_water_well 0.28458588928 0.01091883956148439
## 704 sq_water_well -0.05692071246 0.70803138909271612
## 705 sq_water_tap_unfiltered 0.37268296635 0.00767897644492330
## 706 sq_water_tap_unfiltered 0.13244034482 0.31755980881213985
## 707 sq_water_house_filtration 0.27652632056 0.01100469577269840
## 708 sq_water_house_filtration -0.06234680346 0.73243980970183031
## 709 sq_water_faucet_filter 0.33122239842 0.00611511609349293
## 710 sq_water_faucet_filter 0.09641517113 0.45709065759293344
## 711 sq_water_charcoal_filter 0.28947527111 0.00910866204875724
## 712 sq_water_charcoal_filter -0.08575238784 0.58051667684539865
## 713 sq_water_bottled 0.32964303863 0.01932347445562221
## 714 sq_water_bottled 0.06993004302 0.60590779765292924
## 715 sq_water_none 0.27947592083 0.00867219569146533
## 716 sq_water_none -0.22926941595 0.34669199788674454
## 717 sq_water_other_type 0.33721476837 0.00169010311525126
## 718 sq_water_other_type 0.56525519790 0.00524748730281680
## 719 source -0.50659899172 0.00049247622953313
## 720 source -0.71331858138 0.00000001380182137
## 721 source -0.67687749138 0.00655977315886617
## 722 sex 0.31907869439 0.00293897166877725
## 723 race_eth_label -0.38456128103 0.13013569457742086
## 724 race_eth_label -0.01545994530 0.96442794455498793
## 725 race_eth_label -0.17734261188 0.46598140195591820
## 726 race_eth_label 0.28233582862 0.35335867587746195
## 727 race_final_label 1.13561169417 0.10806768285307439
## 728 race_final_label -0.42530906633 0.39610942244147207
## 729 race_final_label -0.47144851746 0.41428300467104739
## 730 race_final_label 0.30627546779 0.45604501729654012
## 731 race_final_label -0.30743253810 0.00776078864512350
## 732 race_final_label -0.47144851746 0.63586430273091588
## 733 race_final_label -0.42074996522 0.15305394925530741
## 734 race_final_label -0.16756226591 0.66018676901762929
## 735 ethnicity -0.23273436513 0.33347886020210904
## 736 ethnicity 0.28233582862 0.35448966539951243
## 737 rural -0.01757076775 0.92489389560955348
## 738 rural -0.10951764199 0.50284536393231494
## 739 smoking -0.17985647561 0.32177091020604093
## 740 smoking -0.07253382596 0.53558707695853414
## 741 sq_drink_alcohol 0.32796977117 0.03594188193302610
## 742 sq_drink_alcohol 0.02472820551 0.86408706078347330
## 743 sq_drink_alcohol -0.06718987230 0.64121543008337678
## 744 sq_average_drink_per_day -0.27041239489 0.29799235545930247
## 745 sq_average_drink_per_day 0.22209416367 0.59912099850931455
## 746 sq_average_drink_per_day 0.13900622351 0.26608530903584476
## 747 sq_self_hep_b -0.08054960079 0.47899561405698610
## 748 sq_self_hep_b 0.12386342952 0.62208712770531249
## 749 sq_self_hep_c -0.03332494094 0.77046630225326396
## 750 sq_self_hep_c -0.01353349626 0.95592885498327729
## 751 supp_meds_tylenol 0.07478577777 0.84501740098412648
## 752 supp_meds_tylenol -0.27064094184 0.66676216096642582
## 753 supp_meds_steroids 0.14678690410 0.66450267863284707
## 754 supp_meds_steroids -0.05528643906 0.95829689341887170
## 755 sq_water_well -0.07113127787 0.52721094260777346
## 756 sq_water_well -0.02027140684 0.89496451658350551
## 757 sq_water_tap_unfiltered -0.08114647657 0.56352426626744490
## 758 sq_water_tap_unfiltered -0.11994252123 0.36983338632042684
## 759 sq_water_house_filtration -0.02996935033 0.78307622374691188
## 760 sq_water_house_filtration 0.32265583066 0.07896650237649259
## 761 sq_water_faucet_filter 0.09829713530 0.41138610856268598
## 762 sq_water_faucet_filter 0.42992940577 0.00093424336603878
## 763 sq_water_charcoal_filter -0.12033262226 0.27988058283521206
## 764 sq_water_charcoal_filter -0.28953458010 0.06445662344530483
## 765 sq_water_bottled -0.10346737984 0.46520637575138080
## 766 sq_water_bottled -0.10752866806 0.43170112368953550
## 767 sq_water_none -0.01805025876 0.86604968749351308
## 768 sq_water_none 0.31162948316 0.20541041566927390
## 769 sq_water_other_type -0.06912266856 0.52440830804744287
## 770 sq_water_other_type -0.22972534384 0.26277924482141085
## Factor PFAS
## 1 age_at_enrollment pf_hx_s_scld
## 2 bmi pf_hx_s_scld
## 3 trig_mg_d_l pf_hx_s_scld
## 4 age_at_enrollment pfda_scld
## 5 bmi pfda_scld
## 6 trig_mg_d_l pfda_scld
## 7 age_at_enrollment pfna_scld
## 8 bmi pfna_scld
## 9 trig_mg_d_l pfna_scld
## 10 age_at_enrollment pfos_scld
## 11 bmi pfos_scld
## 12 trig_mg_d_l pfos_scld
## 13 age_at_enrollment pf_hp_a_scld
## 14 bmi pf_hp_a_scld
## 15 trig_mg_d_l pf_hp_a_scld
## 16 age_at_enrollment pfbs_scld
## 17 bmi pfbs_scld
## 18 trig_mg_d_l pfbs_scld
## 19 age_at_enrollment pfoa_scld
## 20 bmi pfoa_scld
## 21 trig_mg_d_l pfoa_scld
## 22 age_at_enrollment pf_pe_a_scld
## 23 bmi pf_pe_a_scld
## 24 trig_mg_d_l pf_pe_a_scld
## 25 age_at_enrollment pf_un_a_scld
## 26 bmi pf_un_a_scld
## 27 trig_mg_d_l pf_un_a_scld
## 28 age_at_enrollment pf_hp_s_scld
## 29 bmi pf_hp_s_scld
## 30 trig_mg_d_l pf_hp_s_scld
## 31 age_at_enrollment pf_do_a_scld
## 32 bmi pf_do_a_scld
## 33 trig_mg_d_l pf_do_a_scld
## 34 age_at_enrollment pf_pe_s_scld
## 35 bmi pf_pe_s_scld
## 36 trig_mg_d_l pf_pe_s_scld
## 37 age_at_enrollment pf_hx_a_scld
## 38 bmi pf_hx_a_scld
## 39 trig_mg_d_l pf_hx_a_scld
## 40 age_at_enrollment pfba_scld
## 41 bmi pfba_scld
## 42 trig_mg_d_l pfba_scld
## 43 sourceDUKE pf_hx_s_scld
## 44 sourceNCSU pf_hx_s_scld
## 45 sourceUNC pf_hx_s_scld
## 46 sexMale pf_hx_s_scld
## 47 race_eth_labelNHB pf_hx_s_scld
## 48 race_eth_labelNHO pf_hx_s_scld
## 49 race_eth_labelNHW pf_hx_s_scld
## 50 race_eth_labelUnknown/Not Reported pf_hx_s_scld
## 51 race_final_labelAmerican Indian pf_hx_s_scld
## 52 race_final_labelAmerican Indian/Alaskan Native pf_hx_s_scld
## 53 race_final_labelAsian pf_hx_s_scld
## 54 race_final_labelAsian/Pacific Islander pf_hx_s_scld
## 55 race_final_labelBlack pf_hx_s_scld
## 56 race_final_labelMore than one race pf_hx_s_scld
## 57 race_final_labelOther pf_hx_s_scld
## 58 race_final_labelUnknown/Not Reported pf_hx_s_scld
## 59 ethnicityNot Hispanic pf_hx_s_scld
## 60 ethnicityUnknown/Not Reported pf_hx_s_scld
## 61 ruralLiving in rural area pf_hx_s_scld
## 62 ruralUnknown/Not Reported pf_hx_s_scld
## 63 smokingSmoke or use vape pf_hx_s_scld
## 64 smokingUnknown/Not Reported pf_hx_s_scld
## 65 sq_drink_alcoholNo, former drinker (stopped) pf_hx_s_scld
## 66 sq_drink_alcoholUnknown/Not Reported pf_hx_s_scld
## 67 sq_drink_alcoholYes, current drinker pf_hx_s_scld
## 68 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_s_scld
## 69 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_s_scld
## 70 sq_average_drink_per_dayUnknown/Not Reported pf_hx_s_scld
## 71 sq_self_hep_bUnknown/Not Reported pf_hx_s_scld
## 72 sq_self_hep_bYes pf_hx_s_scld
## 73 sq_self_hep_cUnknown/Not Reported pf_hx_s_scld
## 74 sq_self_hep_cYes pf_hx_s_scld
## 75 supp_meds_tylenolUnknown/Not Reported pf_hx_s_scld
## 76 supp_meds_tylenolYes pf_hx_s_scld
## 77 supp_meds_steroidsUnknown/Not Reported pf_hx_s_scld
## 78 supp_meds_steroidsYes pf_hx_s_scld
## 79 sq_water_wellUnknown/Not Reported pf_hx_s_scld
## 80 sq_water_wellYes pf_hx_s_scld
## 81 sq_water_tap_unfilteredUnknown/Not Reported pf_hx_s_scld
## 82 sq_water_tap_unfilteredYes pf_hx_s_scld
## 83 sq_water_house_filtrationUnknown/Not Reported pf_hx_s_scld
## 84 sq_water_house_filtrationYes pf_hx_s_scld
## 85 sq_water_faucet_filterUnknown/Not Reported pf_hx_s_scld
## 86 sq_water_faucet_filterYes pf_hx_s_scld
## 87 sq_water_charcoal_filterUnknown/Not Reported pf_hx_s_scld
## 88 sq_water_charcoal_filterYes pf_hx_s_scld
## 89 sq_water_bottledUnknown/Not Reported pf_hx_s_scld
## 90 sq_water_bottledYes pf_hx_s_scld
## 91 sq_water_noneUnknown/Not Reported pf_hx_s_scld
## 92 sq_water_noneYes pf_hx_s_scld
## 93 sq_water_other_typeUnknown/Not Reported pf_hx_s_scld
## 94 sq_water_other_typeYes pf_hx_s_scld
## 95 sourceDUKE pfda_scld
## 96 sourceNCSU pfda_scld
## 97 sourceUNC pfda_scld
## 98 sexMale pfda_scld
## 99 race_eth_labelNHB pfda_scld
## 100 race_eth_labelNHO pfda_scld
## 101 race_eth_labelNHW pfda_scld
## 102 race_eth_labelUnknown/Not Reported pfda_scld
## 103 race_final_labelAmerican Indian pfda_scld
## 104 race_final_labelAmerican Indian/Alaskan Native pfda_scld
## 105 race_final_labelAsian pfda_scld
## 106 race_final_labelAsian/Pacific Islander pfda_scld
## 107 race_final_labelBlack pfda_scld
## 108 race_final_labelMore than one race pfda_scld
## 109 race_final_labelOther pfda_scld
## 110 race_final_labelUnknown/Not Reported pfda_scld
## 111 ethnicityNot Hispanic pfda_scld
## 112 ethnicityUnknown/Not Reported pfda_scld
## 113 ruralLiving in rural area pfda_scld
## 114 ruralUnknown/Not Reported pfda_scld
## 115 smokingSmoke or use vape pfda_scld
## 116 smokingUnknown/Not Reported pfda_scld
## 117 sq_drink_alcoholNo, former drinker (stopped) pfda_scld
## 118 sq_drink_alcoholUnknown/Not Reported pfda_scld
## 119 sq_drink_alcoholYes, current drinker pfda_scld
## 120 sq_average_drink_per_day1-2 alcoholic drinks per day pfda_scld
## 121 sq_average_drink_per_day3-4 alcoholic drinks per day pfda_scld
## 122 sq_average_drink_per_dayUnknown/Not Reported pfda_scld
## 123 sq_self_hep_bUnknown/Not Reported pfda_scld
## 124 sq_self_hep_bYes pfda_scld
## 125 sq_self_hep_cUnknown/Not Reported pfda_scld
## 126 sq_self_hep_cYes pfda_scld
## 127 supp_meds_tylenolUnknown/Not Reported pfda_scld
## 128 supp_meds_tylenolYes pfda_scld
## 129 supp_meds_steroidsUnknown/Not Reported pfda_scld
## 130 supp_meds_steroidsYes pfda_scld
## 131 sq_water_wellUnknown/Not Reported pfda_scld
## 132 sq_water_wellYes pfda_scld
## 133 sq_water_tap_unfilteredUnknown/Not Reported pfda_scld
## 134 sq_water_tap_unfilteredYes pfda_scld
## 135 sq_water_house_filtrationUnknown/Not Reported pfda_scld
## 136 sq_water_house_filtrationYes pfda_scld
## 137 sq_water_faucet_filterUnknown/Not Reported pfda_scld
## 138 sq_water_faucet_filterYes pfda_scld
## 139 sq_water_charcoal_filterUnknown/Not Reported pfda_scld
## 140 sq_water_charcoal_filterYes pfda_scld
## 141 sq_water_bottledUnknown/Not Reported pfda_scld
## 142 sq_water_bottledYes pfda_scld
## 143 sq_water_noneUnknown/Not Reported pfda_scld
## 144 sq_water_noneYes pfda_scld
## 145 sq_water_other_typeUnknown/Not Reported pfda_scld
## 146 sq_water_other_typeYes pfda_scld
## 147 sourceDUKE pfna_scld
## 148 sourceNCSU pfna_scld
## 149 sourceUNC pfna_scld
## 150 sexMale pfna_scld
## 151 race_eth_labelNHB pfna_scld
## 152 race_eth_labelNHO pfna_scld
## 153 race_eth_labelNHW pfna_scld
## 154 race_eth_labelUnknown/Not Reported pfna_scld
## 155 race_final_labelAmerican Indian pfna_scld
## 156 race_final_labelAmerican Indian/Alaskan Native pfna_scld
## 157 race_final_labelAsian pfna_scld
## 158 race_final_labelAsian/Pacific Islander pfna_scld
## 159 race_final_labelBlack pfna_scld
## 160 race_final_labelMore than one race pfna_scld
## 161 race_final_labelOther pfna_scld
## 162 race_final_labelUnknown/Not Reported pfna_scld
## 163 ethnicityNot Hispanic pfna_scld
## 164 ethnicityUnknown/Not Reported pfna_scld
## 165 ruralLiving in rural area pfna_scld
## 166 ruralUnknown/Not Reported pfna_scld
## 167 smokingSmoke or use vape pfna_scld
## 168 smokingUnknown/Not Reported pfna_scld
## 169 sq_drink_alcoholNo, former drinker (stopped) pfna_scld
## 170 sq_drink_alcoholUnknown/Not Reported pfna_scld
## 171 sq_drink_alcoholYes, current drinker pfna_scld
## 172 sq_average_drink_per_day1-2 alcoholic drinks per day pfna_scld
## 173 sq_average_drink_per_day3-4 alcoholic drinks per day pfna_scld
## 174 sq_average_drink_per_dayUnknown/Not Reported pfna_scld
## 175 sq_self_hep_bUnknown/Not Reported pfna_scld
## 176 sq_self_hep_bYes pfna_scld
## 177 sq_self_hep_cUnknown/Not Reported pfna_scld
## 178 sq_self_hep_cYes pfna_scld
## 179 supp_meds_tylenolUnknown/Not Reported pfna_scld
## 180 supp_meds_tylenolYes pfna_scld
## 181 supp_meds_steroidsUnknown/Not Reported pfna_scld
## 182 supp_meds_steroidsYes pfna_scld
## 183 sq_water_wellUnknown/Not Reported pfna_scld
## 184 sq_water_wellYes pfna_scld
## 185 sq_water_tap_unfilteredUnknown/Not Reported pfna_scld
## 186 sq_water_tap_unfilteredYes pfna_scld
## 187 sq_water_house_filtrationUnknown/Not Reported pfna_scld
## 188 sq_water_house_filtrationYes pfna_scld
## 189 sq_water_faucet_filterUnknown/Not Reported pfna_scld
## 190 sq_water_faucet_filterYes pfna_scld
## 191 sq_water_charcoal_filterUnknown/Not Reported pfna_scld
## 192 sq_water_charcoal_filterYes pfna_scld
## 193 sq_water_bottledUnknown/Not Reported pfna_scld
## 194 sq_water_bottledYes pfna_scld
## 195 sq_water_noneUnknown/Not Reported pfna_scld
## 196 sq_water_noneYes pfna_scld
## 197 sq_water_other_typeUnknown/Not Reported pfna_scld
## 198 sq_water_other_typeYes pfna_scld
## 199 sourceDUKE pfos_scld
## 200 sourceNCSU pfos_scld
## 201 sourceUNC pfos_scld
## 202 sexMale pfos_scld
## 203 race_eth_labelNHB pfos_scld
## 204 race_eth_labelNHO pfos_scld
## 205 race_eth_labelNHW pfos_scld
## 206 race_eth_labelUnknown/Not Reported pfos_scld
## 207 race_final_labelAmerican Indian pfos_scld
## 208 race_final_labelAmerican Indian/Alaskan Native pfos_scld
## 209 race_final_labelAsian pfos_scld
## 210 race_final_labelAsian/Pacific Islander pfos_scld
## 211 race_final_labelBlack pfos_scld
## 212 race_final_labelMore than one race pfos_scld
## 213 race_final_labelOther pfos_scld
## 214 race_final_labelUnknown/Not Reported pfos_scld
## 215 ethnicityNot Hispanic pfos_scld
## 216 ethnicityUnknown/Not Reported pfos_scld
## 217 ruralLiving in rural area pfos_scld
## 218 ruralUnknown/Not Reported pfos_scld
## 219 smokingSmoke or use vape pfos_scld
## 220 smokingUnknown/Not Reported pfos_scld
## 221 sq_drink_alcoholNo, former drinker (stopped) pfos_scld
## 222 sq_drink_alcoholUnknown/Not Reported pfos_scld
## 223 sq_drink_alcoholYes, current drinker pfos_scld
## 224 sq_average_drink_per_day1-2 alcoholic drinks per day pfos_scld
## 225 sq_average_drink_per_day3-4 alcoholic drinks per day pfos_scld
## 226 sq_average_drink_per_dayUnknown/Not Reported pfos_scld
## 227 sq_self_hep_bUnknown/Not Reported pfos_scld
## 228 sq_self_hep_bYes pfos_scld
## 229 sq_self_hep_cUnknown/Not Reported pfos_scld
## 230 sq_self_hep_cYes pfos_scld
## 231 supp_meds_tylenolUnknown/Not Reported pfos_scld
## 232 supp_meds_tylenolYes pfos_scld
## 233 supp_meds_steroidsUnknown/Not Reported pfos_scld
## 234 supp_meds_steroidsYes pfos_scld
## 235 sq_water_wellUnknown/Not Reported pfos_scld
## 236 sq_water_wellYes pfos_scld
## 237 sq_water_tap_unfilteredUnknown/Not Reported pfos_scld
## 238 sq_water_tap_unfilteredYes pfos_scld
## 239 sq_water_house_filtrationUnknown/Not Reported pfos_scld
## 240 sq_water_house_filtrationYes pfos_scld
## 241 sq_water_faucet_filterUnknown/Not Reported pfos_scld
## 242 sq_water_faucet_filterYes pfos_scld
## 243 sq_water_charcoal_filterUnknown/Not Reported pfos_scld
## 244 sq_water_charcoal_filterYes pfos_scld
## 245 sq_water_bottledUnknown/Not Reported pfos_scld
## 246 sq_water_bottledYes pfos_scld
## 247 sq_water_noneUnknown/Not Reported pfos_scld
## 248 sq_water_noneYes pfos_scld
## 249 sq_water_other_typeUnknown/Not Reported pfos_scld
## 250 sq_water_other_typeYes pfos_scld
## 251 sourceDUKE pf_hp_a_scld
## 252 sourceNCSU pf_hp_a_scld
## 253 sourceUNC pf_hp_a_scld
## 254 sexMale pf_hp_a_scld
## 255 race_eth_labelNHB pf_hp_a_scld
## 256 race_eth_labelNHO pf_hp_a_scld
## 257 race_eth_labelNHW pf_hp_a_scld
## 258 race_eth_labelUnknown/Not Reported pf_hp_a_scld
## 259 race_final_labelAmerican Indian pf_hp_a_scld
## 260 race_final_labelAmerican Indian/Alaskan Native pf_hp_a_scld
## 261 race_final_labelAsian pf_hp_a_scld
## 262 race_final_labelAsian/Pacific Islander pf_hp_a_scld
## 263 race_final_labelBlack pf_hp_a_scld
## 264 race_final_labelMore than one race pf_hp_a_scld
## 265 race_final_labelOther pf_hp_a_scld
## 266 race_final_labelUnknown/Not Reported pf_hp_a_scld
## 267 ethnicityNot Hispanic pf_hp_a_scld
## 268 ethnicityUnknown/Not Reported pf_hp_a_scld
## 269 ruralLiving in rural area pf_hp_a_scld
## 270 ruralUnknown/Not Reported pf_hp_a_scld
## 271 smokingSmoke or use vape pf_hp_a_scld
## 272 smokingUnknown/Not Reported pf_hp_a_scld
## 273 sq_drink_alcoholNo, former drinker (stopped) pf_hp_a_scld
## 274 sq_drink_alcoholUnknown/Not Reported pf_hp_a_scld
## 275 sq_drink_alcoholYes, current drinker pf_hp_a_scld
## 276 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_a_scld
## 277 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_a_scld
## 278 sq_average_drink_per_dayUnknown/Not Reported pf_hp_a_scld
## 279 sq_self_hep_bUnknown/Not Reported pf_hp_a_scld
## 280 sq_self_hep_bYes pf_hp_a_scld
## 281 sq_self_hep_cUnknown/Not Reported pf_hp_a_scld
## 282 sq_self_hep_cYes pf_hp_a_scld
## 283 supp_meds_tylenolUnknown/Not Reported pf_hp_a_scld
## 284 supp_meds_tylenolYes pf_hp_a_scld
## 285 supp_meds_steroidsUnknown/Not Reported pf_hp_a_scld
## 286 supp_meds_steroidsYes pf_hp_a_scld
## 287 sq_water_wellUnknown/Not Reported pf_hp_a_scld
## 288 sq_water_wellYes pf_hp_a_scld
## 289 sq_water_tap_unfilteredUnknown/Not Reported pf_hp_a_scld
## 290 sq_water_tap_unfilteredYes pf_hp_a_scld
## 291 sq_water_house_filtrationUnknown/Not Reported pf_hp_a_scld
## 292 sq_water_house_filtrationYes pf_hp_a_scld
## 293 sq_water_faucet_filterUnknown/Not Reported pf_hp_a_scld
## 294 sq_water_faucet_filterYes pf_hp_a_scld
## 295 sq_water_charcoal_filterUnknown/Not Reported pf_hp_a_scld
## 296 sq_water_charcoal_filterYes pf_hp_a_scld
## 297 sq_water_bottledUnknown/Not Reported pf_hp_a_scld
## 298 sq_water_bottledYes pf_hp_a_scld
## 299 sq_water_noneUnknown/Not Reported pf_hp_a_scld
## 300 sq_water_noneYes pf_hp_a_scld
## 301 sq_water_other_typeUnknown/Not Reported pf_hp_a_scld
## 302 sq_water_other_typeYes pf_hp_a_scld
## 303 sourceDUKE pfbs_scld
## 304 sourceNCSU pfbs_scld
## 305 sourceUNC pfbs_scld
## 306 sexMale pfbs_scld
## 307 race_eth_labelNHB pfbs_scld
## 308 race_eth_labelNHO pfbs_scld
## 309 race_eth_labelNHW pfbs_scld
## 310 race_eth_labelUnknown/Not Reported pfbs_scld
## 311 race_final_labelAmerican Indian pfbs_scld
## 312 race_final_labelAmerican Indian/Alaskan Native pfbs_scld
## 313 race_final_labelAsian pfbs_scld
## 314 race_final_labelAsian/Pacific Islander pfbs_scld
## 315 race_final_labelBlack pfbs_scld
## 316 race_final_labelMore than one race pfbs_scld
## 317 race_final_labelOther pfbs_scld
## 318 race_final_labelUnknown/Not Reported pfbs_scld
## 319 ethnicityNot Hispanic pfbs_scld
## 320 ethnicityUnknown/Not Reported pfbs_scld
## 321 ruralLiving in rural area pfbs_scld
## 322 ruralUnknown/Not Reported pfbs_scld
## 323 smokingSmoke or use vape pfbs_scld
## 324 smokingUnknown/Not Reported pfbs_scld
## 325 sq_drink_alcoholNo, former drinker (stopped) pfbs_scld
## 326 sq_drink_alcoholUnknown/Not Reported pfbs_scld
## 327 sq_drink_alcoholYes, current drinker pfbs_scld
## 328 sq_average_drink_per_day1-2 alcoholic drinks per day pfbs_scld
## 329 sq_average_drink_per_day3-4 alcoholic drinks per day pfbs_scld
## 330 sq_average_drink_per_dayUnknown/Not Reported pfbs_scld
## 331 sq_self_hep_bUnknown/Not Reported pfbs_scld
## 332 sq_self_hep_bYes pfbs_scld
## 333 sq_self_hep_cUnknown/Not Reported pfbs_scld
## 334 sq_self_hep_cYes pfbs_scld
## 335 supp_meds_tylenolUnknown/Not Reported pfbs_scld
## 336 supp_meds_tylenolYes pfbs_scld
## 337 supp_meds_steroidsUnknown/Not Reported pfbs_scld
## 338 supp_meds_steroidsYes pfbs_scld
## 339 sq_water_wellUnknown/Not Reported pfbs_scld
## 340 sq_water_wellYes pfbs_scld
## 341 sq_water_tap_unfilteredUnknown/Not Reported pfbs_scld
## 342 sq_water_tap_unfilteredYes pfbs_scld
## 343 sq_water_house_filtrationUnknown/Not Reported pfbs_scld
## 344 sq_water_house_filtrationYes pfbs_scld
## 345 sq_water_faucet_filterUnknown/Not Reported pfbs_scld
## 346 sq_water_faucet_filterYes pfbs_scld
## 347 sq_water_charcoal_filterUnknown/Not Reported pfbs_scld
## 348 sq_water_charcoal_filterYes pfbs_scld
## 349 sq_water_bottledUnknown/Not Reported pfbs_scld
## 350 sq_water_bottledYes pfbs_scld
## 351 sq_water_noneUnknown/Not Reported pfbs_scld
## 352 sq_water_noneYes pfbs_scld
## 353 sq_water_other_typeUnknown/Not Reported pfbs_scld
## 354 sq_water_other_typeYes pfbs_scld
## 355 sourceDUKE pfoa_scld
## 356 sourceNCSU pfoa_scld
## 357 sourceUNC pfoa_scld
## 358 sexMale pfoa_scld
## 359 race_eth_labelNHB pfoa_scld
## 360 race_eth_labelNHO pfoa_scld
## 361 race_eth_labelNHW pfoa_scld
## 362 race_eth_labelUnknown/Not Reported pfoa_scld
## 363 race_final_labelAmerican Indian pfoa_scld
## 364 race_final_labelAmerican Indian/Alaskan Native pfoa_scld
## 365 race_final_labelAsian pfoa_scld
## 366 race_final_labelAsian/Pacific Islander pfoa_scld
## 367 race_final_labelBlack pfoa_scld
## 368 race_final_labelMore than one race pfoa_scld
## 369 race_final_labelOther pfoa_scld
## 370 race_final_labelUnknown/Not Reported pfoa_scld
## 371 ethnicityNot Hispanic pfoa_scld
## 372 ethnicityUnknown/Not Reported pfoa_scld
## 373 ruralLiving in rural area pfoa_scld
## 374 ruralUnknown/Not Reported pfoa_scld
## 375 smokingSmoke or use vape pfoa_scld
## 376 smokingUnknown/Not Reported pfoa_scld
## 377 sq_drink_alcoholNo, former drinker (stopped) pfoa_scld
## 378 sq_drink_alcoholUnknown/Not Reported pfoa_scld
## 379 sq_drink_alcoholYes, current drinker pfoa_scld
## 380 sq_average_drink_per_day1-2 alcoholic drinks per day pfoa_scld
## 381 sq_average_drink_per_day3-4 alcoholic drinks per day pfoa_scld
## 382 sq_average_drink_per_dayUnknown/Not Reported pfoa_scld
## 383 sq_self_hep_bUnknown/Not Reported pfoa_scld
## 384 sq_self_hep_bYes pfoa_scld
## 385 sq_self_hep_cUnknown/Not Reported pfoa_scld
## 386 sq_self_hep_cYes pfoa_scld
## 387 supp_meds_tylenolUnknown/Not Reported pfoa_scld
## 388 supp_meds_tylenolYes pfoa_scld
## 389 supp_meds_steroidsUnknown/Not Reported pfoa_scld
## 390 supp_meds_steroidsYes pfoa_scld
## 391 sq_water_wellUnknown/Not Reported pfoa_scld
## 392 sq_water_wellYes pfoa_scld
## 393 sq_water_tap_unfilteredUnknown/Not Reported pfoa_scld
## 394 sq_water_tap_unfilteredYes pfoa_scld
## 395 sq_water_house_filtrationUnknown/Not Reported pfoa_scld
## 396 sq_water_house_filtrationYes pfoa_scld
## 397 sq_water_faucet_filterUnknown/Not Reported pfoa_scld
## 398 sq_water_faucet_filterYes pfoa_scld
## 399 sq_water_charcoal_filterUnknown/Not Reported pfoa_scld
## 400 sq_water_charcoal_filterYes pfoa_scld
## 401 sq_water_bottledUnknown/Not Reported pfoa_scld
## 402 sq_water_bottledYes pfoa_scld
## 403 sq_water_noneUnknown/Not Reported pfoa_scld
## 404 sq_water_noneYes pfoa_scld
## 405 sq_water_other_typeUnknown/Not Reported pfoa_scld
## 406 sq_water_other_typeYes pfoa_scld
## 407 sourceDUKE pf_pe_a_scld
## 408 sourceNCSU pf_pe_a_scld
## 409 sourceUNC pf_pe_a_scld
## 410 sexMale pf_pe_a_scld
## 411 race_eth_labelNHB pf_pe_a_scld
## 412 race_eth_labelNHO pf_pe_a_scld
## 413 race_eth_labelNHW pf_pe_a_scld
## 414 race_eth_labelUnknown/Not Reported pf_pe_a_scld
## 415 race_final_labelAmerican Indian pf_pe_a_scld
## 416 race_final_labelAmerican Indian/Alaskan Native pf_pe_a_scld
## 417 race_final_labelAsian pf_pe_a_scld
## 418 race_final_labelAsian/Pacific Islander pf_pe_a_scld
## 419 race_final_labelBlack pf_pe_a_scld
## 420 race_final_labelMore than one race pf_pe_a_scld
## 421 race_final_labelOther pf_pe_a_scld
## 422 race_final_labelUnknown/Not Reported pf_pe_a_scld
## 423 ethnicityNot Hispanic pf_pe_a_scld
## 424 ethnicityUnknown/Not Reported pf_pe_a_scld
## 425 ruralLiving in rural area pf_pe_a_scld
## 426 ruralUnknown/Not Reported pf_pe_a_scld
## 427 smokingSmoke or use vape pf_pe_a_scld
## 428 smokingUnknown/Not Reported pf_pe_a_scld
## 429 sq_drink_alcoholNo, former drinker (stopped) pf_pe_a_scld
## 430 sq_drink_alcoholUnknown/Not Reported pf_pe_a_scld
## 431 sq_drink_alcoholYes, current drinker pf_pe_a_scld
## 432 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_a_scld
## 433 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_a_scld
## 434 sq_average_drink_per_dayUnknown/Not Reported pf_pe_a_scld
## 435 sq_self_hep_bUnknown/Not Reported pf_pe_a_scld
## 436 sq_self_hep_bYes pf_pe_a_scld
## 437 sq_self_hep_cUnknown/Not Reported pf_pe_a_scld
## 438 sq_self_hep_cYes pf_pe_a_scld
## 439 supp_meds_tylenolUnknown/Not Reported pf_pe_a_scld
## 440 supp_meds_tylenolYes pf_pe_a_scld
## 441 supp_meds_steroidsUnknown/Not Reported pf_pe_a_scld
## 442 supp_meds_steroidsYes pf_pe_a_scld
## 443 sq_water_wellUnknown/Not Reported pf_pe_a_scld
## 444 sq_water_wellYes pf_pe_a_scld
## 445 sq_water_tap_unfilteredUnknown/Not Reported pf_pe_a_scld
## 446 sq_water_tap_unfilteredYes pf_pe_a_scld
## 447 sq_water_house_filtrationUnknown/Not Reported pf_pe_a_scld
## 448 sq_water_house_filtrationYes pf_pe_a_scld
## 449 sq_water_faucet_filterUnknown/Not Reported pf_pe_a_scld
## 450 sq_water_faucet_filterYes pf_pe_a_scld
## 451 sq_water_charcoal_filterUnknown/Not Reported pf_pe_a_scld
## 452 sq_water_charcoal_filterYes pf_pe_a_scld
## 453 sq_water_bottledUnknown/Not Reported pf_pe_a_scld
## 454 sq_water_bottledYes pf_pe_a_scld
## 455 sq_water_noneUnknown/Not Reported pf_pe_a_scld
## 456 sq_water_noneYes pf_pe_a_scld
## 457 sq_water_other_typeUnknown/Not Reported pf_pe_a_scld
## 458 sq_water_other_typeYes pf_pe_a_scld
## 459 sourceDUKE pf_un_a_scld
## 460 sourceNCSU pf_un_a_scld
## 461 sourceUNC pf_un_a_scld
## 462 sexMale pf_un_a_scld
## 463 race_eth_labelNHB pf_un_a_scld
## 464 race_eth_labelNHO pf_un_a_scld
## 465 race_eth_labelNHW pf_un_a_scld
## 466 race_eth_labelUnknown/Not Reported pf_un_a_scld
## 467 race_final_labelAmerican Indian pf_un_a_scld
## 468 race_final_labelAmerican Indian/Alaskan Native pf_un_a_scld
## 469 race_final_labelAsian pf_un_a_scld
## 470 race_final_labelAsian/Pacific Islander pf_un_a_scld
## 471 race_final_labelBlack pf_un_a_scld
## 472 race_final_labelMore than one race pf_un_a_scld
## 473 race_final_labelOther pf_un_a_scld
## 474 race_final_labelUnknown/Not Reported pf_un_a_scld
## 475 ethnicityNot Hispanic pf_un_a_scld
## 476 ethnicityUnknown/Not Reported pf_un_a_scld
## 477 ruralLiving in rural area pf_un_a_scld
## 478 ruralUnknown/Not Reported pf_un_a_scld
## 479 smokingSmoke or use vape pf_un_a_scld
## 480 smokingUnknown/Not Reported pf_un_a_scld
## 481 sq_drink_alcoholNo, former drinker (stopped) pf_un_a_scld
## 482 sq_drink_alcoholUnknown/Not Reported pf_un_a_scld
## 483 sq_drink_alcoholYes, current drinker pf_un_a_scld
## 484 sq_average_drink_per_day1-2 alcoholic drinks per day pf_un_a_scld
## 485 sq_average_drink_per_day3-4 alcoholic drinks per day pf_un_a_scld
## 486 sq_average_drink_per_dayUnknown/Not Reported pf_un_a_scld
## 487 sq_self_hep_bUnknown/Not Reported pf_un_a_scld
## 488 sq_self_hep_bYes pf_un_a_scld
## 489 sq_self_hep_cUnknown/Not Reported pf_un_a_scld
## 490 sq_self_hep_cYes pf_un_a_scld
## 491 supp_meds_tylenolUnknown/Not Reported pf_un_a_scld
## 492 supp_meds_tylenolYes pf_un_a_scld
## 493 supp_meds_steroidsUnknown/Not Reported pf_un_a_scld
## 494 supp_meds_steroidsYes pf_un_a_scld
## 495 sq_water_wellUnknown/Not Reported pf_un_a_scld
## 496 sq_water_wellYes pf_un_a_scld
## 497 sq_water_tap_unfilteredUnknown/Not Reported pf_un_a_scld
## 498 sq_water_tap_unfilteredYes pf_un_a_scld
## 499 sq_water_house_filtrationUnknown/Not Reported pf_un_a_scld
## 500 sq_water_house_filtrationYes pf_un_a_scld
## 501 sq_water_faucet_filterUnknown/Not Reported pf_un_a_scld
## 502 sq_water_faucet_filterYes pf_un_a_scld
## 503 sq_water_charcoal_filterUnknown/Not Reported pf_un_a_scld
## 504 sq_water_charcoal_filterYes pf_un_a_scld
## 505 sq_water_bottledUnknown/Not Reported pf_un_a_scld
## 506 sq_water_bottledYes pf_un_a_scld
## 507 sq_water_noneUnknown/Not Reported pf_un_a_scld
## 508 sq_water_noneYes pf_un_a_scld
## 509 sq_water_other_typeUnknown/Not Reported pf_un_a_scld
## 510 sq_water_other_typeYes pf_un_a_scld
## 511 sourceDUKE pf_hp_s_scld
## 512 sourceNCSU pf_hp_s_scld
## 513 sourceUNC pf_hp_s_scld
## 514 sexMale pf_hp_s_scld
## 515 race_eth_labelNHB pf_hp_s_scld
## 516 race_eth_labelNHO pf_hp_s_scld
## 517 race_eth_labelNHW pf_hp_s_scld
## 518 race_eth_labelUnknown/Not Reported pf_hp_s_scld
## 519 race_final_labelAmerican Indian pf_hp_s_scld
## 520 race_final_labelAmerican Indian/Alaskan Native pf_hp_s_scld
## 521 race_final_labelAsian pf_hp_s_scld
## 522 race_final_labelAsian/Pacific Islander pf_hp_s_scld
## 523 race_final_labelBlack pf_hp_s_scld
## 524 race_final_labelMore than one race pf_hp_s_scld
## 525 race_final_labelOther pf_hp_s_scld
## 526 race_final_labelUnknown/Not Reported pf_hp_s_scld
## 527 ethnicityNot Hispanic pf_hp_s_scld
## 528 ethnicityUnknown/Not Reported pf_hp_s_scld
## 529 ruralLiving in rural area pf_hp_s_scld
## 530 ruralUnknown/Not Reported pf_hp_s_scld
## 531 smokingSmoke or use vape pf_hp_s_scld
## 532 smokingUnknown/Not Reported pf_hp_s_scld
## 533 sq_drink_alcoholNo, former drinker (stopped) pf_hp_s_scld
## 534 sq_drink_alcoholUnknown/Not Reported pf_hp_s_scld
## 535 sq_drink_alcoholYes, current drinker pf_hp_s_scld
## 536 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_s_scld
## 537 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_s_scld
## 538 sq_average_drink_per_dayUnknown/Not Reported pf_hp_s_scld
## 539 sq_self_hep_bUnknown/Not Reported pf_hp_s_scld
## 540 sq_self_hep_bYes pf_hp_s_scld
## 541 sq_self_hep_cUnknown/Not Reported pf_hp_s_scld
## 542 sq_self_hep_cYes pf_hp_s_scld
## 543 supp_meds_tylenolUnknown/Not Reported pf_hp_s_scld
## 544 supp_meds_tylenolYes pf_hp_s_scld
## 545 supp_meds_steroidsUnknown/Not Reported pf_hp_s_scld
## 546 supp_meds_steroidsYes pf_hp_s_scld
## 547 sq_water_wellUnknown/Not Reported pf_hp_s_scld
## 548 sq_water_wellYes pf_hp_s_scld
## 549 sq_water_tap_unfilteredUnknown/Not Reported pf_hp_s_scld
## 550 sq_water_tap_unfilteredYes pf_hp_s_scld
## 551 sq_water_house_filtrationUnknown/Not Reported pf_hp_s_scld
## 552 sq_water_house_filtrationYes pf_hp_s_scld
## 553 sq_water_faucet_filterUnknown/Not Reported pf_hp_s_scld
## 554 sq_water_faucet_filterYes pf_hp_s_scld
## 555 sq_water_charcoal_filterUnknown/Not Reported pf_hp_s_scld
## 556 sq_water_charcoal_filterYes pf_hp_s_scld
## 557 sq_water_bottledUnknown/Not Reported pf_hp_s_scld
## 558 sq_water_bottledYes pf_hp_s_scld
## 559 sq_water_noneUnknown/Not Reported pf_hp_s_scld
## 560 sq_water_noneYes pf_hp_s_scld
## 561 sq_water_other_typeUnknown/Not Reported pf_hp_s_scld
## 562 sq_water_other_typeYes pf_hp_s_scld
## 563 sourceDUKE pf_do_a_scld
## 564 sourceNCSU pf_do_a_scld
## 565 sourceUNC pf_do_a_scld
## 566 sexMale pf_do_a_scld
## 567 race_eth_labelNHB pf_do_a_scld
## 568 race_eth_labelNHO pf_do_a_scld
## 569 race_eth_labelNHW pf_do_a_scld
## 570 race_eth_labelUnknown/Not Reported pf_do_a_scld
## 571 race_final_labelAmerican Indian pf_do_a_scld
## 572 race_final_labelAmerican Indian/Alaskan Native pf_do_a_scld
## 573 race_final_labelAsian pf_do_a_scld
## 574 race_final_labelAsian/Pacific Islander pf_do_a_scld
## 575 race_final_labelBlack pf_do_a_scld
## 576 race_final_labelMore than one race pf_do_a_scld
## 577 race_final_labelOther pf_do_a_scld
## 578 race_final_labelUnknown/Not Reported pf_do_a_scld
## 579 ethnicityNot Hispanic pf_do_a_scld
## 580 ethnicityUnknown/Not Reported pf_do_a_scld
## 581 ruralLiving in rural area pf_do_a_scld
## 582 ruralUnknown/Not Reported pf_do_a_scld
## 583 smokingSmoke or use vape pf_do_a_scld
## 584 smokingUnknown/Not Reported pf_do_a_scld
## 585 sq_drink_alcoholNo, former drinker (stopped) pf_do_a_scld
## 586 sq_drink_alcoholUnknown/Not Reported pf_do_a_scld
## 587 sq_drink_alcoholYes, current drinker pf_do_a_scld
## 588 sq_average_drink_per_day1-2 alcoholic drinks per day pf_do_a_scld
## 589 sq_average_drink_per_day3-4 alcoholic drinks per day pf_do_a_scld
## 590 sq_average_drink_per_dayUnknown/Not Reported pf_do_a_scld
## 591 sq_self_hep_bUnknown/Not Reported pf_do_a_scld
## 592 sq_self_hep_bYes pf_do_a_scld
## 593 sq_self_hep_cUnknown/Not Reported pf_do_a_scld
## 594 sq_self_hep_cYes pf_do_a_scld
## 595 supp_meds_tylenolUnknown/Not Reported pf_do_a_scld
## 596 supp_meds_tylenolYes pf_do_a_scld
## 597 supp_meds_steroidsUnknown/Not Reported pf_do_a_scld
## 598 supp_meds_steroidsYes pf_do_a_scld
## 599 sq_water_wellUnknown/Not Reported pf_do_a_scld
## 600 sq_water_wellYes pf_do_a_scld
## 601 sq_water_tap_unfilteredUnknown/Not Reported pf_do_a_scld
## 602 sq_water_tap_unfilteredYes pf_do_a_scld
## 603 sq_water_house_filtrationUnknown/Not Reported pf_do_a_scld
## 604 sq_water_house_filtrationYes pf_do_a_scld
## 605 sq_water_faucet_filterUnknown/Not Reported pf_do_a_scld
## 606 sq_water_faucet_filterYes pf_do_a_scld
## 607 sq_water_charcoal_filterUnknown/Not Reported pf_do_a_scld
## 608 sq_water_charcoal_filterYes pf_do_a_scld
## 609 sq_water_bottledUnknown/Not Reported pf_do_a_scld
## 610 sq_water_bottledYes pf_do_a_scld
## 611 sq_water_noneUnknown/Not Reported pf_do_a_scld
## 612 sq_water_noneYes pf_do_a_scld
## 613 sq_water_other_typeUnknown/Not Reported pf_do_a_scld
## 614 sq_water_other_typeYes pf_do_a_scld
## 615 sourceDUKE pf_pe_s_scld
## 616 sourceNCSU pf_pe_s_scld
## 617 sourceUNC pf_pe_s_scld
## 618 sexMale pf_pe_s_scld
## 619 race_eth_labelNHB pf_pe_s_scld
## 620 race_eth_labelNHO pf_pe_s_scld
## 621 race_eth_labelNHW pf_pe_s_scld
## 622 race_eth_labelUnknown/Not Reported pf_pe_s_scld
## 623 race_final_labelAmerican Indian pf_pe_s_scld
## 624 race_final_labelAmerican Indian/Alaskan Native pf_pe_s_scld
## 625 race_final_labelAsian pf_pe_s_scld
## 626 race_final_labelAsian/Pacific Islander pf_pe_s_scld
## 627 race_final_labelBlack pf_pe_s_scld
## 628 race_final_labelMore than one race pf_pe_s_scld
## 629 race_final_labelOther pf_pe_s_scld
## 630 race_final_labelUnknown/Not Reported pf_pe_s_scld
## 631 ethnicityNot Hispanic pf_pe_s_scld
## 632 ethnicityUnknown/Not Reported pf_pe_s_scld
## 633 ruralLiving in rural area pf_pe_s_scld
## 634 ruralUnknown/Not Reported pf_pe_s_scld
## 635 smokingSmoke or use vape pf_pe_s_scld
## 636 smokingUnknown/Not Reported pf_pe_s_scld
## 637 sq_drink_alcoholNo, former drinker (stopped) pf_pe_s_scld
## 638 sq_drink_alcoholUnknown/Not Reported pf_pe_s_scld
## 639 sq_drink_alcoholYes, current drinker pf_pe_s_scld
## 640 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_s_scld
## 641 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_s_scld
## 642 sq_average_drink_per_dayUnknown/Not Reported pf_pe_s_scld
## 643 sq_self_hep_bUnknown/Not Reported pf_pe_s_scld
## 644 sq_self_hep_bYes pf_pe_s_scld
## 645 sq_self_hep_cUnknown/Not Reported pf_pe_s_scld
## 646 sq_self_hep_cYes pf_pe_s_scld
## 647 supp_meds_tylenolUnknown/Not Reported pf_pe_s_scld
## 648 supp_meds_tylenolYes pf_pe_s_scld
## 649 supp_meds_steroidsUnknown/Not Reported pf_pe_s_scld
## 650 supp_meds_steroidsYes pf_pe_s_scld
## 651 sq_water_wellUnknown/Not Reported pf_pe_s_scld
## 652 sq_water_wellYes pf_pe_s_scld
## 653 sq_water_tap_unfilteredUnknown/Not Reported pf_pe_s_scld
## 654 sq_water_tap_unfilteredYes pf_pe_s_scld
## 655 sq_water_house_filtrationUnknown/Not Reported pf_pe_s_scld
## 656 sq_water_house_filtrationYes pf_pe_s_scld
## 657 sq_water_faucet_filterUnknown/Not Reported pf_pe_s_scld
## 658 sq_water_faucet_filterYes pf_pe_s_scld
## 659 sq_water_charcoal_filterUnknown/Not Reported pf_pe_s_scld
## 660 sq_water_charcoal_filterYes pf_pe_s_scld
## 661 sq_water_bottledUnknown/Not Reported pf_pe_s_scld
## 662 sq_water_bottledYes pf_pe_s_scld
## 663 sq_water_noneUnknown/Not Reported pf_pe_s_scld
## 664 sq_water_noneYes pf_pe_s_scld
## 665 sq_water_other_typeUnknown/Not Reported pf_pe_s_scld
## 666 sq_water_other_typeYes pf_pe_s_scld
## 667 sourceDUKE pf_hx_a_scld
## 668 sourceNCSU pf_hx_a_scld
## 669 sourceUNC pf_hx_a_scld
## 670 sexMale pf_hx_a_scld
## 671 race_eth_labelNHB pf_hx_a_scld
## 672 race_eth_labelNHO pf_hx_a_scld
## 673 race_eth_labelNHW pf_hx_a_scld
## 674 race_eth_labelUnknown/Not Reported pf_hx_a_scld
## 675 race_final_labelAmerican Indian pf_hx_a_scld
## 676 race_final_labelAmerican Indian/Alaskan Native pf_hx_a_scld
## 677 race_final_labelAsian pf_hx_a_scld
## 678 race_final_labelAsian/Pacific Islander pf_hx_a_scld
## 679 race_final_labelBlack pf_hx_a_scld
## 680 race_final_labelMore than one race pf_hx_a_scld
## 681 race_final_labelOther pf_hx_a_scld
## 682 race_final_labelUnknown/Not Reported pf_hx_a_scld
## 683 ethnicityNot Hispanic pf_hx_a_scld
## 684 ethnicityUnknown/Not Reported pf_hx_a_scld
## 685 ruralLiving in rural area pf_hx_a_scld
## 686 ruralUnknown/Not Reported pf_hx_a_scld
## 687 smokingSmoke or use vape pf_hx_a_scld
## 688 smokingUnknown/Not Reported pf_hx_a_scld
## 689 sq_drink_alcoholNo, former drinker (stopped) pf_hx_a_scld
## 690 sq_drink_alcoholUnknown/Not Reported pf_hx_a_scld
## 691 sq_drink_alcoholYes, current drinker pf_hx_a_scld
## 692 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_a_scld
## 693 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_a_scld
## 694 sq_average_drink_per_dayUnknown/Not Reported pf_hx_a_scld
## 695 sq_self_hep_bUnknown/Not Reported pf_hx_a_scld
## 696 sq_self_hep_bYes pf_hx_a_scld
## 697 sq_self_hep_cUnknown/Not Reported pf_hx_a_scld
## 698 sq_self_hep_cYes pf_hx_a_scld
## 699 supp_meds_tylenolUnknown/Not Reported pf_hx_a_scld
## 700 supp_meds_tylenolYes pf_hx_a_scld
## 701 supp_meds_steroidsUnknown/Not Reported pf_hx_a_scld
## 702 supp_meds_steroidsYes pf_hx_a_scld
## 703 sq_water_wellUnknown/Not Reported pf_hx_a_scld
## 704 sq_water_wellYes pf_hx_a_scld
## 705 sq_water_tap_unfilteredUnknown/Not Reported pf_hx_a_scld
## 706 sq_water_tap_unfilteredYes pf_hx_a_scld
## 707 sq_water_house_filtrationUnknown/Not Reported pf_hx_a_scld
## 708 sq_water_house_filtrationYes pf_hx_a_scld
## 709 sq_water_faucet_filterUnknown/Not Reported pf_hx_a_scld
## 710 sq_water_faucet_filterYes pf_hx_a_scld
## 711 sq_water_charcoal_filterUnknown/Not Reported pf_hx_a_scld
## 712 sq_water_charcoal_filterYes pf_hx_a_scld
## 713 sq_water_bottledUnknown/Not Reported pf_hx_a_scld
## 714 sq_water_bottledYes pf_hx_a_scld
## 715 sq_water_noneUnknown/Not Reported pf_hx_a_scld
## 716 sq_water_noneYes pf_hx_a_scld
## 717 sq_water_other_typeUnknown/Not Reported pf_hx_a_scld
## 718 sq_water_other_typeYes pf_hx_a_scld
## 719 sourceDUKE pfba_scld
## 720 sourceNCSU pfba_scld
## 721 sourceUNC pfba_scld
## 722 sexMale pfba_scld
## 723 race_eth_labelNHB pfba_scld
## 724 race_eth_labelNHO pfba_scld
## 725 race_eth_labelNHW pfba_scld
## 726 race_eth_labelUnknown/Not Reported pfba_scld
## 727 race_final_labelAmerican Indian pfba_scld
## 728 race_final_labelAmerican Indian/Alaskan Native pfba_scld
## 729 race_final_labelAsian pfba_scld
## 730 race_final_labelAsian/Pacific Islander pfba_scld
## 731 race_final_labelBlack pfba_scld
## 732 race_final_labelMore than one race pfba_scld
## 733 race_final_labelOther pfba_scld
## 734 race_final_labelUnknown/Not Reported pfba_scld
## 735 ethnicityNot Hispanic pfba_scld
## 736 ethnicityUnknown/Not Reported pfba_scld
## 737 ruralLiving in rural area pfba_scld
## 738 ruralUnknown/Not Reported pfba_scld
## 739 smokingSmoke or use vape pfba_scld
## 740 smokingUnknown/Not Reported pfba_scld
## 741 sq_drink_alcoholNo, former drinker (stopped) pfba_scld
## 742 sq_drink_alcoholUnknown/Not Reported pfba_scld
## 743 sq_drink_alcoholYes, current drinker pfba_scld
## 744 sq_average_drink_per_day1-2 alcoholic drinks per day pfba_scld
## 745 sq_average_drink_per_day3-4 alcoholic drinks per day pfba_scld
## 746 sq_average_drink_per_dayUnknown/Not Reported pfba_scld
## 747 sq_self_hep_bUnknown/Not Reported pfba_scld
## 748 sq_self_hep_bYes pfba_scld
## 749 sq_self_hep_cUnknown/Not Reported pfba_scld
## 750 sq_self_hep_cYes pfba_scld
## 751 supp_meds_tylenolUnknown/Not Reported pfba_scld
## 752 supp_meds_tylenolYes pfba_scld
## 753 supp_meds_steroidsUnknown/Not Reported pfba_scld
## 754 supp_meds_steroidsYes pfba_scld
## 755 sq_water_wellUnknown/Not Reported pfba_scld
## 756 sq_water_wellYes pfba_scld
## 757 sq_water_tap_unfilteredUnknown/Not Reported pfba_scld
## 758 sq_water_tap_unfilteredYes pfba_scld
## 759 sq_water_house_filtrationUnknown/Not Reported pfba_scld
## 760 sq_water_house_filtrationYes pfba_scld
## 761 sq_water_faucet_filterUnknown/Not Reported pfba_scld
## 762 sq_water_faucet_filterYes pfba_scld
## 763 sq_water_charcoal_filterUnknown/Not Reported pfba_scld
## 764 sq_water_charcoal_filterYes pfba_scld
## 765 sq_water_bottledUnknown/Not Reported pfba_scld
## 766 sq_water_bottledYes pfba_scld
## 767 sq_water_noneUnknown/Not Reported pfba_scld
## 768 sq_water_noneYes pfba_scld
## 769 sq_water_other_typeUnknown/Not Reported pfba_scld
## 770 sq_water_other_typeYes pfba_scld##
ggplot(all_results, aes(x = "", y = Coeff)) +
geom_boxplot(fill = "lightblue", color = "blue") +
labs(title = "Box Plot of Coefficients", y = "Coefficient") +
theme_minimal()
##
all_results <- all_results %>%
mutate(
# Create a formatted table using gt
results_df %>%
gt() %>%
tab_header(
title = "Associations between exposures and AST/ALT Ratio From Simple LR"
) %>%
cols_label(
term = "Variables",
estimate = "Estimate",
std.error = "Standard Error",
statistic = "Statistic",
p.value = "P-value"
) %>%
fmt_number(
columns = c(estimate, std.error, statistic, p.value),
decimals = 3
) %>%
tab_source_note(
source_note = "Significant results with p-value < 0.05"
)| Associations between exposures and AST/ALT Ratio From Simple LR | ||||
| Variables | Estimate | Standard Error | Statistic | P-value |
|---|---|---|---|---|
| pf_hx_s | −0.138 | 0.061 | −2.264 | 0.024 |
| pfda | −0.327 | 0.301 | −1.086 | 0.278 |
| pfna | −0.048 | 0.184 | −0.262 | 0.794 |
| pfos | −0.045 | 0.015 | −2.998 | 0.003 |
| pf_hp_a | 0.557 | 1.832 | 0.304 | 0.761 |
| pfbs | 0.000 | 0.914 | 0.000 | 1.000 |
| pfoa | −0.001 | 0.067 | −0.020 | 0.984 |
| pf_pe_a | −0.096 | 3.570 | −0.027 | 0.979 |
| pf_un_a | −1.029 | 0.677 | −1.520 | 0.129 |
| pf_hp_s | −1.898 | 0.573 | −3.314 | 0.001 |
| pf_do_a | −1.112 | 2.855 | −0.390 | 0.697 |
| pf_pe_s | −5.312 | 3.166 | −1.678 | 0.094 |
| pf_hx_a | 32.737 | 9.377 | 3.491 | 0.001 |
| pfba | −0.601 | 1.636 | −0.368 | 0.713 |
| sourceEmory | 0.503 | 0.283 | 1.774 | 0.077 |
| sourceNCSU | −0.173 | 0.239 | −0.721 | 0.471 |
| sourceUNC | 2.167 | 0.476 | 4.553 | 0.000 |
| age_at_enrollment | −0.001 | 0.011 | −0.117 | 0.907 |
| sexMale | −0.369 | 0.209 | −1.772 | 0.077 |
| race_eth_labelNHB | −0.033 | 0.504 | −0.066 | 0.947 |
| race_eth_labelNHO | −0.488 | 0.682 | −0.716 | 0.474 |
| race_eth_labelNHW | −0.227 | 0.484 | −0.468 | 0.640 |
| race_eth_labelUnknown/Not Reported | −0.320 | 0.612 | −0.523 | 0.601 |
| race_final_labelAmerican Indian/Alaskan Native | −1.860 | 1.693 | −1.099 | 0.273 |
| race_final_labelAsian | −2.071 | 1.693 | −1.223 | 0.222 |
| race_final_labelAsian/Pacific Islander | −1.180 | 1.514 | −0.779 | 0.436 |
| race_final_labelBlack | −0.661 | 1.324 | −0.499 | 0.618 |
| race_final_labelMore than one race | −1.982 | 2.271 | −0.873 | 0.383 |
| race_final_labelOther | 0.325 | 1.425 | 0.228 | 0.820 |
| race_final_labelUnknown/Not Reported | −1.441 | 1.514 | −0.952 | 0.342 |
| race_final_labelWhite | −0.823 | 1.317 | −0.625 | 0.532 |
| ethnicityNot Hispanic | −0.181 | 0.477 | −0.379 | 0.705 |
| ethnicityUnknown/Not Reported | −0.320 | 0.611 | −0.524 | 0.601 |
| ruralLiving in rural area | −0.380 | 0.357 | −1.065 | 0.288 |
| ruralUnknown/Not Reported | −0.064 | 0.318 | −0.201 | 0.841 |
| smokingSmoke or use vape | 1.525 | 0.342 | 4.460 | 0.000 |
| smokingUnknown/Not Reported | −0.049 | 0.220 | −0.222 | 0.825 |
| sq_drink_alcoholNo, never drinker | −0.381 | 0.306 | −1.245 | 0.214 |
| sq_drink_alcoholUnknown/Not Reported | −0.536 | 0.289 | −1.854 | 0.065 |
| sq_drink_alcoholYes, current drinker | −0.428 | 0.285 | −1.499 | 0.135 |
| sq_average_drink_per_day3-4 alcoholic drinks per day | −0.005 | 0.878 | −0.005 | 0.996 |
| sq_average_drink_per_dayLess than 1 alcoholic drink per day | 0.097 | 0.486 | 0.200 | 0.841 |
| sq_average_drink_per_dayUnknown/Not Reported | 0.170 | 0.455 | 0.373 | 0.709 |
| sq_self_hep_bUnknown/Not Reported | −0.144 | 0.219 | −0.661 | 0.509 |
| sq_self_hep_bYes | 0.780 | 0.466 | 1.675 | 0.095 |
| sq_self_hep_cUnknown/Not Reported | −0.116 | 0.214 | −0.541 | 0.589 |
| sq_self_hep_cYes | 1.926 | 0.455 | 4.228 | 0.000 |
| supp_meds_tylenolUnknown/Not Reported | −0.678 | 0.765 | −0.886 | 0.376 |
| supp_meds_tylenolYes | −1.185 | 1.314 | −0.902 | 0.368 |
| supp_meds_steroidsUnknown/Not Reported | −0.515 | 0.710 | −0.725 | 0.469 |
| supp_meds_steroidsYes | −1.669 | 1.987 | −0.840 | 0.402 |
| sq_water_wellUnknown/Not Reported | −0.191 | 0.217 | −0.879 | 0.380 |
| sq_water_wellYes | 0.030 | 0.298 | 0.102 | 0.919 |
| sq_water_tap_unfilteredUnknown/Not Reported | −0.575 | 0.273 | −2.106 | 0.036 |
| sq_water_tap_unfilteredYes | −0.336 | 0.258 | −1.301 | 0.194 |
| sq_water_house_filtrationUnknown/Not Reported | −0.162 | 0.210 | −0.771 | 0.441 |
| sq_water_house_filtrationYes | 0.376 | 0.356 | 1.056 | 0.291 |
| sq_water_faucet_filterUnknown/Not Reported | −0.333 | 0.234 | −1.427 | 0.155 |
| sq_water_faucet_filterYes | −0.339 | 0.252 | −1.345 | 0.179 |
| sq_water_charcoal_filterUnknown/Not Reported | −0.196 | 0.216 | −0.908 | 0.364 |
| sq_water_charcoal_filterYes | −0.262 | 0.300 | −0.871 | 0.384 |
| sq_water_bottledUnknown/Not Reported | −0.154 | 0.271 | −0.566 | 0.572 |
| sq_water_bottledYes | 0.390 | 0.261 | 1.491 | 0.137 |
| sq_water_noneUnknown/Not Reported | −0.171 | 0.206 | −0.829 | 0.408 |
| sq_water_noneYes | −0.272 | 0.498 | −0.546 | 0.585 |
| sq_water_other_typeUnknown/Not Reported | −0.131 | 0.210 | −0.624 | 0.533 |
| sq_water_other_typeYes | −0.443 | 0.382 | −1.159 | 0.247 |
| Significant results with p-value < 0.05 | ||||
Association of PFAS mixture with ALT and AST related outcomes
res <- readxl::read_xlsx(fs::path(dir_result,
"mixture_analysis_alt_ast_outcomes.xlsx"))
res %>%
filter(feature == "alt_u_l") %>%
dplyr::select(parameter, type, coef_ci, test_statistic,
p_value, adjusted_pval)%>%
gt() %>%
tab_header(
title = "Associations between PFAS mixture and ALT From qgcomp"
) %>%
cols_label(
parameter = "Parameter",
type = "PFAS mixture",
coef_ci = "Effect Estimate (95% CI)",
test_statistic = "Statistic",
p_value = "P-value",
adjusted_pval = "Adjusted P-value",
) | Associations between PFAS mixture and ALT From qgcomp | |||||
| Parameter | PFAS mixture | Effect Estimate (95% CI) | Statistic | P-value | Adjusted P-value |
|---|---|---|---|---|---|
| q = 3 | legacy_pfas | -0.980 (-4.170, 2.210) | -0.6018355 | 0.5477032 | 0.5477032 |
| q = 4 | legacy_pfas | -1.180 (-3.460, 1.100) | -1.0143351 | 0.3111774 | 0.3111774 |
| q = 5 | legacy_pfas | -0.527 (-2.330, 1.280) | -0.5715785 | 0.5680026 | 0.5680026 |
| q = 6 | emerging_pfas | 1.360 (-3.150, 5.880) | 0.5919853 | 0.5542670 | 0.9605448 |
res %>%
filter(feature == "ast_u_l") %>%
dplyr::select(parameter, type, coef_ci, test_statistic,
p_value, adjusted_pval)%>%
gt() %>%
tab_header(
title = "Associations between PFAS mixture and AST From qgcomp"
) %>%
cols_label(
parameter = "Parameter",
type = "PFAS mixture",
coef_ci = "Effect Estimate (95% CI)",
test_statistic = "Statistic",
p_value = "P-value",
adjusted_pval = "Adjusted P-value",
) | Associations between PFAS mixture and AST From qgcomp | |||||
| Associations between PFAS mixture and ALT(defined by 29 in males and 19 in females) From qgcomp | |||||
| Parameter | PFAS mixture | Effect Estimate (95% CI) | Statistic | P-value | Adjusted P-value |
|---|---|---|---|---|---|
| q = 3 | legacy_pfas | -0.154 (-0.771, 0.463) | -0.4882128 | 0.6253991 | 0.9760588 |
| q = 4 | legacy_pfas | -0.262 (-0.745, 0.220) | -1.0653051 | 0.2867379 | 0.6411472 |
| q = 5 | legacy_pfas | -0.084 (-0.480, 0.312) | -0.4175731 | 0.6762593 | 0.7821957 |
| q = 3 | emerging_pfas | 0.352 (-0.310, 1.010) | 1.0425545 | 0.2971547 | 0.3087793 |
| q = 4 | emerging_pfas | 0.170 (-0.388, 0.729) | 0.5981991 | 0.5497071 | 0.7329428 |
| q = 5 | emerging_pfas | 0.195 (-0.191, 0.580) | 0.9893718 | 0.3224813 | 0.6449625 |
| q = 6 | emerging_pfas | 0.243 (-0.322, 0.809) | 0.8434947 | 0.3989518 | 0.5319358 |
| Associations between PFAS mixture and AST(defined by 29 in males and 19 in females) From qgcomp | |||||
| Parameter | PFAS mixture | Effect Estimate (95% CI) | Statistic | P-value | Adjusted P-value |
|---|---|---|---|---|---|
| q = 3 | legacy_pfas | -0.045 (-0.213, 0.123) | -0.52547490 | 0.5992531 | 0.9760588 |
| q = 4 | legacy_pfas | -0.042 (-0.158, 0.074) | -0.70491935 | 0.4808604 | 0.6411472 |
| q = 5 | legacy_pfas | -0.021 (-0.110, 0.068) | -0.46079091 | 0.6449486 | 0.7821957 |
| q = 3 | emerging_pfas | 0.089 (-0.083, 0.261) | 1.01778684 | 0.3087793 | 0.3087793 |
| q = 4 | emerging_pfas | 0.005 (-0.133, 0.144) | 0.07326034 | 0.9415990 | 0.9415990 |
| q = 5 | emerging_pfas | 0.004 (-0.096, 0.104) | 0.08240405 | 0.9343254 | 0.9343254 |
| q = 6 | emerging_pfas | 0.071 (-0.019, 0.161) | 1.54632885 | 0.1220252 | 0.2440503 |
res %>%
filter(feature == "alt_cat2") %>%
dplyr::select(parameter, type, coef_ci, test_statistic,
p_value, adjusted_pval)%>%
gt() %>%
tab_header(
title = "Associations between PFAS mixture and ALT(defined by 33 in males and 25 in females) From qgcomp"
) %>%
cols_label(
parameter = "Parameter",
type = "PFAS mixture",
coef_ci = "Effect Estimate (95% CI)",
test_statistic = "Statistic",
p_value = "P-value",
adjusted_pval = "Adjusted P-value",
) | Associations between PFAS mixture and ALT(defined by 33 in males and 25 in females) From qgcomp | |||||
| Parameter | PFAS mixture | Effect Estimate (95% CI) | Statistic | P-value | Adjusted P-value |
|---|---|---|---|---|---|
| q = 3 | legacy_pfas | -0.021 (-1.380, 1.340) | -0.03001029 | 0.97605885 | 0.97605885 |
| q = 4 | legacy_pfas | -0.366 (-1.360, 0.630) | -0.71984547 | 0.47162015 | 0.64114724 |
| q = 5 | legacy_pfas | -0.107 (-0.868, 0.654) | -0.27645875 | 0.78219574 | 0.78219574 |
| q = 3 | emerging_pfas | 0.889 (-0.068, 1.850) | 1.82022084 | 0.06872538 | 0.27490152 |
| q = 4 | emerging_pfas | 0.559 (-0.059, 1.180) | 1.77415073 | 0.07603822 | 0.30415289 |
| q = 5 | emerging_pfas | 0.513 (0.068, 0.958) | 2.25843777 | 0.02391838 | 0.09567352 |
| q = 6 | emerging_pfas | 0.155 (-0.747, 1.060) | 0.33755774 | 0.73569649 | 0.73569649 |
res %>%
filter(feature == "ast_cat2") %>%
dplyr::select(parameter, type, coef_ci, test_statistic,
p_value, adjusted_pval)%>%
gt() %>%
tab_header(
title = "Associations between PFAS mixture and AST(defined by 33 in males and 25 in females) From qgcomp"
) %>%
cols_label(
parameter = "Parameter",
type = "PFAS mixture",
coef_ci = "Effect Estimate (95% CI)",
test_statistic = "Statistic",
p_value = "P-value",
adjusted_pval = "Adjusted P-value",
) | Associations between PFAS mixture and AST(defined by 33 in males and 25 in females) From qgcomp | |||||
| Parameter | PFAS mixture | Effect Estimate (95% CI) | Statistic | P-value | Adjusted P-value |
|---|---|---|---|---|---|
| q = 3 | legacy_pfas | 0.025 (-0.192, 0.242) | 0.2247876 | 0.82214451 | 0.97605885 |
| q = 4 | legacy_pfas | -0.009 (-0.159, 0.141) | -0.1195588 | 0.90483263 | 0.90483263 |
| q = 5 | legacy_pfas | 0.038 (-0.084, 0.161) | 0.6129471 | 0.53991135 | 0.78219574 |
| q = 3 | emerging_pfas | 0.160 (-0.062, 0.383) | 1.4120066 | 0.15794803 | 0.30877927 |
| q = 4 | emerging_pfas | 0.064 (-0.135, 0.264) | 0.6309945 | 0.52804414 | 0.73294276 |
| q = 5 | emerging_pfas | 0.040 (-0.087, 0.166) | 0.6176704 | 0.53679263 | 0.71572350 |
| q = 6 | emerging_pfas | 0.133 (0.028, 0.239) | 2.4732770 | 0.01338803 | 0.05355213 |
